Journal articles: 'Rule-based agents'

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Jago, Mark. "Epistemic Logic for Rule-Based Agents." Journal of Logic, Language and Information 18, no. 1 (July 19, 2008): 131–58. http://dx.doi.org/10.1007/s10849-008-9071-8.

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Alechina, Natasha, Mark Jago, and Brian Logan. "Preference-based belief revision for rule-based agents." Synthese 165, no. 2 (July 3, 2008): 159–77. http://dx.doi.org/10.1007/s11229-008-9364-0.

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PASCHKE, ADRIAN, and HAROLD BOLEY. "RULE RESPONDER: RULE-BASED AGENTS FOR THE SEMANTIC-PRAGMATIC WEB." International Journal on Artificial Intelligence Tools 20, no. 06 (December 2011): 1043–81. http://dx.doi.org/10.1142/s0218213011000528.

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Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

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Dietrich, J., A. Kozlenkov, M. Schroeder, and G. Wagner. "Rule-based agents for the semantic web." Electronic Commerce Research and Applications 2, no. 4 (December 2003): 323–38. http://dx.doi.org/10.1016/s1567-4223(03)00041-3.

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Canaan, Rodrigo, Xianbo Gao, Youjin Chung, Julian Togelius, Andy Nealen, and Stefan Menzel. "Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 31–37. http://dx.doi.org/10.1609/aiide.v16i1.7404.

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Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.

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Jha, S., and M. Hassan. "Building agents for rule-based intrusion detection system." Computer Communications 25, no. 15 (September 2002): 1366–73. http://dx.doi.org/10.1016/s0140-3664(02)00038-5.

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Riveret, Régis, Antonino Rotolo, and Giovanni Sartor. "Probabilistic rule-based argumentation for norm-governed learning agents." Artificial Intelligence and Law 20, no. 4 (November 2012): 383–420. http://dx.doi.org/10.1007/s10506-012-9134-7.

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Mukhopadhyay, Arpan, Ravi R. Mazumdar, and Rahul Roy. "Majority Rule Based Opinion Dynamics with Biased and Stubborn Agents." ACM SIGMETRICS Performance Evaluation Review 44, no. 1 (June 30, 2016): 385–86. http://dx.doi.org/10.1145/2964791.2901488.

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Lopes Costa Monte Alto, Helio Henrique, Ayslan Trevizan Possebom, Miriam Mariela Mercedes Morveli Espinoza, and Cesar Augusto Tacla. "A rule-based argumentation framework for distributed contextual reasoning in dynamic environments." DYNA 88, no. 217 (May 11, 2021): 120–30. http://dx.doi.org/10.15446/dyna.v88n217.90858.

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In this study, we tackled the problem of distributed reasoning in environments in which agents may have incomplete and inconsistent knowledge. Conflicts between agents are resolved through defeasible argumentation-based semantics with a preference function. Support for dynamic environments, where agents constantly enter and leave the system, was achieved by means of rules whose premises can be held by arbitrary agents. Moreover, we presented a formalism that enables agents to share information about their current situation or focus when issuing queries to other agents. This is necessary in environments where agents have a partial view of the world and must be able to cooperate with one another to reach conclusions. Hence, we presented the formalization of a multi-agent system and the argument construction and semantics that define its reasoning approach. Using example scenarios, we demonstrated that our system enables the modeling of a broader range of scenarios than related work.

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Ksystra, Katerina, and Petros Stefaneas. "Formal analysis and verification support for reactive rule-based Web agents." International Journal of Web Information Systems 12, no. 4 (November 7, 2016): 418–47. http://dx.doi.org/10.1108/ijwis-04-2016-0024.

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Purpose Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules. Design/methodology/approach To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides. Findings The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies. Originality/value Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.

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Freixas, Josep. "An Aggregation Rule Based on the Binomial Distribution." Mathematics 10, no. 23 (November 23, 2022): 4418. http://dx.doi.org/10.3390/math10234418.

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Many decision-making situations require the evaluation of several voters or agents. In a situation where voters evaluate candidates, the question arises of how best to aggregate evaluations so as to compare the candidates. The aim of this work is to propose a method of aggregating the evaluations of the voters, which has outstanding properties and serve as a potential evaluative tool in many contexts. Ordered weighted averages is a family of rules appropriate for studying this problem. In this paper, I propose as a solution an ordered weighted average that satisfies compelling properties and whose weights are derived from the binomial distribution.

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Arentze, Theo, Frank Hofman, Henk van Mourik, and Harry Timmermans. "ALBATROSS: Multiagent, Rule-Based Model of Activity Pattern Decisions." Transportation Research Record: Journal of the Transportation Research Board 1706, no. 1 (January 2000): 136–44. http://dx.doi.org/10.3141/1706-16.

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The development of ALBATROSS: A Learning-Based, Transportation-Oriented Simulation System is summarized. This activity-based model of activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, and with whom, and the transport mode involved. In addition, various logical, temporal, spatial, spatial-temporal, and institutional constraints are incorporated in the model. The conceptual underpinnings of the model, its architecture, the functionality of its key agents, data collection, and model performance are discussed.

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POGGI, AGOSTINO. "INTEGRATING OBJECT-ORIENTED AND RULE-BASED PROGRAMMING FOR BUILDING AGENT SYSTEMS." International Journal on Artificial Intelligence Tools 07, no. 02 (June 1998): 221–38. http://dx.doi.org/10.1142/s0218213098000123.

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To facilitate the success of agent technology, we need suitable tools both to reduce the effort in the development of agent systems and to obtain efficient implementations of them. This paper presents an agent-oriented language for the development of multi-agent systems, called ALL (Agent Level Language), that should offer both the two features. In fact, on the one hand, ALL integrates object-oriented and rule based programming to simplify the definition of agent systems and the management of interactions between agents. On the other hand, ALL can be translated into C++, Common Lisp and Java code and can encapsulate objects written in these three languages allowing the reuse of a large amount of pre-existent software and the realization of efficient applications in a large set of (different) domains. ALL rules derive from YACC rules and can simplify the management of agents interaction because they are suitable for the parsing of messages and because they can be reused thanks to object-oriented inheritance.

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Paschke, Adrian. "Provalets: Component-Based Mobile Agents as Microservices for Rule-Based Data Access, Processing and Analytics." Business & Information Systems Engineering 58, no. 5 (August 18, 2016): 329–40. http://dx.doi.org/10.1007/s12599-016-0447-z.

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Teófilo, Luís, Luís Reis, Henrique Cardoso, and Pedro Mendes. "Rule based strategies for large extensive-form games: A specification language for No-Limit Texas Hold’em agents." Computer Science and Information Systems 11, no. 4 (2014): 1249–69. http://dx.doi.org/10.2298/csis130921029t.

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Poker is used to measure progresses in extensive-form games research due to its unique characteristics: it is a game where playing agents have to deal with incomplete information and stochastic scenarios and a large number of decision points. The development of Poker agents has seen significant advances in one-on-one matches but there are still no consistent results in multiplayer and in games against human experts. In order to allow for experts to aid the improvement of the agents? performance, we have created a high-level strategy specification language. To support strategy definition, we have also developed an intuitive graphical tool. Additionally, we have also created a strategy inferring system, based on a dynamically weighted Euclidean distance. This approach was validated through the creation of simple agents and by successfully inferring strategies from 10 human players. The created agents were able to beat previously developed mid-level agents by a good profit margin.

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M. Abdelmohsen, Sherif. "A BIM-based framework for outdoor circulation rule checking." Revista de Arquitectura 18, no. 26 (August 13, 2014): 20. http://dx.doi.org/10.5354/0719-5427.2012.32537.

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This paper introduces a BIM-based framework for outdoor circulation rule checking from a geometric modeling perspective. The paper extracts rules and patterns of circulation and interaction based on site planning standards and codes. It then identifies high-level operators and 3D low-level operations involved in applicable circulation rules, and suggest methods for implementation. Mechanisms of integration between high-level operators and low-level operations are defined for different domains of rule checking, based on three types of datasets: 1) BIM objects in outdoor settings, including agents like pedestrians, bikes, vehicles, and other objects; 2) attributes and behavior of objects, and 3) interrelations among objects, including agent-agent or agent-object intersection conflicts, agent-agent or agent-object visual access, unobstructed access, and outdoor lighting and shading.

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Liu, Xiao Lan, and Wu Yi Zhang. "Modeling and Simulation of CAS for Safety of Agricultural Products under Government Regulation." Applied Mechanics and Materials 444-445 (October 2013): 1680–84. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1680.

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The safety of agricultural products is a hot issue concerned by the whole society. Safety system of agricultural products is regarded as complex adaptive system in this paper and after analyzing the attributes and behaviors of Government Agent and Farmer Agent, these Agents are all seemed as adaptive agents and their behaviors follow the stimulus--response model. Besides, rule of obtaining profit, rule of changing production strategy, rule of changing honesty and rule of farmer moving are also established. Then based on these rules, a computer program for the system model has been done by Java on Swarm. And through changing the relevant parameters and variables in the model, the measures to improve the safety situation of agricultural products in China are obtained.

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Dunin-Kęplicz, Barbara, Anh Nguyen, and Andrzej Szałas. "A layered rule-based architecture for approximate knowledge fusion?" Computer Science and Information Systems 7, no. 3 (2010): 617–42. http://dx.doi.org/10.2298/csis100209015d.

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In this paper we present a framework for fusing approximate knowledge obtained from various distributed, heterogenous knowledge sources. This issue is substantial in modeling multi-agent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. In paper [5] we have focused on defining general mechanism for knowledge fusion. Next, the techniques ensuring tractability of fusing knowledge expressed as a Horn subset of propositional dynamic logic were developed in [13,16]. Propositional logics may seem too weak to be useful in real-world applications. On the other hand, propositional languages may be viewed as sublanguages of first-order logics which serve as a natural tool to define concepts in the spirit of description logics [2]. These notions may be further used to define various ontologies, like e.g. those applicable in the Semantic Web. Taking this step, we propose a framework, in which our Horn subset of dynamic logic is combined with deductive database technology. This synthesis is formally implemented in the framework of HSPDL architecture. The resulting knowledge fusion rules are naturally applicable to real-world data.

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Löwe, Peter. "Introducing the new GRASS module g.infer for data-driven rule-based applications." Geoinformatics FCE CTU 8 (October 14, 2012): 17–28. http://dx.doi.org/10.14311/gi.8.2.

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This paper introduces the new GRASS GIS add-on module g.infer. The module enables rule-based analysis and workflow management in GRASS GIS, via data-driven inference processes based on the expert system shell CLIPS. The paper discusses the theoretical and developmental background that will help prepare the reader to use the module for Knowledge Engineering applications. In addition, potential application scenarios are sketched out, ranging from the rule-driven formulation of nontrivial GIS-classification tasks and GIS workflows to ontology management and intelligent software agents.

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Kulkarni, Anand J., and K. Tai. "A Probability Collectives Approach with a Feasibility-Based Rule for Constrained Optimization." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/980216.

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This paper demonstrates an attempt to incorporate a simple and generic constraint handling technique to the Probability Collectives (PC) approach for solving constrained optimization problems. The approach of PC optimizes any complex system by decomposing it into smaller subsystems and further treats them in a distributed and decentralized way. These subsystems can be viewed as a Multi-Agent System with rational and self-interested agents optimizing their local goals. However, as there is no inherent constraint handling capability in the PC approach, a real challenge is to take into account constraints and at the same time make the agents work collectively avoiding the tragedy of commons to optimize the global/system objective. At the core of the PC optimization methodology are the concepts of Deterministic Annealing in Statistical Physics, Game Theory and Nash Equilibrium. Moreover, a rule-based procedure is incorporated to handle solutions based on the number of constraints violated and drive the convergence towards feasibility. Two specially developed cases of the Circle Packing Problem with known solutions are solved and the true optimum results are obtained at reasonable computational costs. The proposed algorithm is shown to be sufficiently robust, and strengths and weaknesses of the methodology are also discussed.

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Stein, Harald D. "ALLOCATION RULES WITH OUTSIDE OPTION IN COOPERATION GAMES WITH TIME‐INCONSISTENCY." Journal of Business Economics and Management 11, no. 1 (March 31, 2010): 56–96. http://dx.doi.org/10.3846/jbem.2010.04.

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In game theory agents have the possibility to make binding agreements. The agents are assumed to determine their strategies based on intended but bounded rationality. The field of strategic games provides the possibility to an agent to understand the optimality of his behaviour. In coalition and network games stability, Pareto‐efficiency and fairness of agreements is investigated. The paper shows the relationship between the different fields of game theory in the case of 3 agents. On that basis it shows the ubiquity of time‐inconsistency in dynamic setting due to bounded rationality, deception and environment changes. The paper explains why allocation rules like the Shapley‐based Aumann‐Drèze‐value and the Myerson‐value for coalition structures must be modified in dynamic setting in order to consider the influence of excluded agents, the outside option. An accordingly modified allocation rule is introduced and investigated. It is shown that the “Aumann‐Drèze‐value” and the “Myerson‐value for coalition structures” remains relevant for the case that the switching of the partner is connected with high costs. It is shown through the example of enterprise cooperation in supply chains that low partner switching costs require the introduced allocation rule that considers the outside option. Santrauka Žaidimu teorijoje agentai turi galimybe sudaryti isipareigojančius susitarimus. Agentai, kaip yra mano‐ma, numato savo strategijas riboto racionalumo salygomis. Strateginiu žaidimu sritis sudaro galimybe agentui suvokti optimalios elgsenos krypti. Straipsnyje tyrinejamas ryšys tarp skirtingu žaidimu teo‐rijos sričiu tuo atveju, kai susitarimuose dalyvauja trys agentai. Atskleidžiamas neišvengiamas agentu elgsenos nesuderinamumas del riboto racionalumo, apgavysčiu bei aplinkos pokyčiu. Straipsnyje aiš‐kinama, kad žaidimu teorijos numatomos agentu susitarimu taisykles turetu būti modifikuotos siekiant ivertinti papildomu susitarimu alternatyvu galimybe.

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Stein, Harald D. "ALLOCATION RULES WITH OUTSIDE OPTION IN COOPERATION GAMES WITH TIME‐INCONSISTENCY." Journal of Business Economics and Management 11, no. 1 (March 31, 2010): 56–96. http://dx.doi.org/10.3846/jbem.202010.04.

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In game theory agents have the possibility to make binding agreements. The agents are assumed to determine their strategies based on intended but bounded rationality. The field of strategic games provides the possibility to an agent to understand the optimality of his behaviour. In coalition and network games stability, Pareto‐efficiency and fairness of agreements is investigated. The paper shows the relationship between the different fields of game theory in the case of 3 agents. On that basis it shows the ubiquity of time‐inconsistency in dynamic setting due to bounded rationality, deception and environment changes. The paper explains why allocation rules like the Shapley‐based Aumann‐Drèze‐value and the Myerson‐value for coalition structures must be modified in dynamic setting in order to consider the influence of excluded agents, the outside option. An accordingly modified allocation rule is introduced and investigated. It is shown that the “Aumann‐Drèze‐value” and the “Myerson‐value for coalition structures” remains relevant for the case that the switching of the partner is connected with high costs. It is shown through the example of enterprise cooperation in supply chains that low partner switching costs require the introduced allocation rule that considers the outside option. Santrauka Žaidimu teorijoje agentai turi galimybe sudaryti isipareigojančius susitarimus. Agentai, kaip yra mano‐ma, numato savo strategijas riboto racionalumo salygomis. Strateginiu žaidimu sritis sudaro galimybe agentui suvokti optimalios elgsenos krypti. Straipsnyje tyrinejamas ryšys tarp skirtingu žaidimu teo‐rijos sričiu tuo atveju, kai susitarimuose dalyvauja trys agentai. Atskleidžiamas neišvengiamas agentu elgsenos nesuderinamumas del riboto racionalumo, apgavysčiu bei aplinkos pokyčiu. Straipsnyje aiš‐kinama, kad žaidimu teorijos numatomos agentu susitarimu taisykles turetu būti modifikuotos siekiant ivertinti papildomu susitarimu alternatyvu galimybe.

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Bishop, I. D., and H. R. Gimblett. "Management of Recreational Areas: GIS, Autonomous Agents, and Virtual Reality." Environment and Planning B: Planning and Design 27, no. 3 (June 2000): 423–35. http://dx.doi.org/10.1068/b2637.

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Management of recreational activity in areas that are culturally or ecologically sensitive requires knowledge, and effective management, of recreationists' behaviour. In this paper we explore the role of spatial information systems, spatial modelling, and virtual reality in the analysis and prediction of visitor location and movement patterns. The quantitative modelling of the time spent by visitors on various aspects of the site attractions and of visitor conflict has not been widely attempted, having only recently become possible because of greater computer power, better spatial data storage options, and new modelling paradigms. Rule-driven autonomous agents can be used as surrogates for human visitors. Behavioural rules can be derived and calibrated from visitor surveys. This is, however, an expensive and time-consuming process and testing of people's decisions in a virtual environment may provide sufficient information for rule definition. Once a rule-set is determined, the autonomous agents move over a GIS-based model of the landscape. Rendering algorithms determine what an individual agent is able to “see”. Based on the established rules, this and other factors (such as tiredness) determine behavioural choice. Recording of model runs to file allows managers to undertake additional analysis to quantify and explore the influence of alternative management options on recreationist movement, congestion, and crowding. Through the GIS, impacts such as erosion can also be modelled. In the longer term the combined models can become part of a decision support system for sustainable tourism in fragile environments.

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Shi, Yong Gui. "Research on Element Model of Enterprise Network." Advanced Materials Research 472-475 (February 2012): 3140–43. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3140.

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Enterprise network is a group of interconnected enterprises and institutions, which connected with information technology. In the process of sharing resources, exchanging resources and transferring resources, they formed a variety of relations based on the same background of social culture and mutual trust in order to respond to market changes quickly. As a kind of special social network, enterprise network also has four basic elements of the social networks, which namely the structure elements, resource elements, rule elements and dynamic elements. Structural elements is the most intuitive expression form of enterprise network, including the structure of enterprise network agents and the relation between enterprise network agents; rule elements affects or determines the behavior rules of enterprise network agents, which also affects the flow of resource elements in enterprise network; resource element is the resources that belong to enterprise network agents or they access to; dynamic elements is the external environment that affects the enterprise network.

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Bredin, J. L., D. C. Parkes, and Q. Duong. "Chain: A Dynamic Double Auction Framework for Matching Patient Agents." Journal of Artificial Intelligence Research 30 (September 30, 2007): 133–79. http://dx.doi.org/10.1613/jair.2303.

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In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with ``zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.

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Pan, Shao Ming, Hong Li, and Ge Tang. "Research on Dynamic Statistics for Spatial Data Access Laws Based on P2P." Applied Mechanics and Materials 380-384 (August 2013): 1995–98. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1995.

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The strategy of hierarchical storage can be adjusted utilizing the access rule of the spatial data, which will significantly improve system performance of spatial data services. The access and distribution rule of the spatial data based on Hotmap and Zipf-like cannot reflect its global information. A dynamic statistics algorithm for the distribution rule of the spatial data based on P2P is proposed in this paper. The service capabilities of the service nodes are calculated in our algorithm. The node agents with good service capabilities are chosen preferentially in the group. At the same time, the size of group is controlled. The experimental results show that the performance of our algorithm can be improved by about 28% compared with the algorithm of random nodes. The algorithm can meet the need of dynamic statistics in large scale distribution environment with high efficiency.

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Dent, S., B. Zee, J. Dancey, A. Hanauske, J. Wanders, and E. Eisenhauer. "Application of a New Multinomial Phase II Stopping Rule Using Response and Early Progression." Journal of Clinical Oncology 19, no. 3 (February 1, 2001): 785–91. http://dx.doi.org/10.1200/jco.2001.19.3.785.

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PURPOSE: A multinomial stopping rule had previously been developed that incorporated both objective response and early progression into decisions to stop or continue phase II trials of anticancer agents. The purpose of this study was to apply the multinomial rule to two independent sets of phase II data to assess its utility in appropriately recommending early trial closure as compared with other stopping rules. MATERIALS AND METHODS: Data from completed phase II trials of the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) and European Organization for Research and Treatment of Cancer Early Clinical Studies Group (ECSG) formed the basis of the study. Based on observed results for each trial, the recommendation of the multinomial stopping rule was applied, as was the recommendation of the actual stopping rule used (Fleming or Gehan). The appropriateness of the recommendations was evaluated based on interpretation of final study results. RESULTS: The standard and multinomial rules disagreed on early stopping in one of 16 NCIC CTG trials and in seven of 23 ECSG trials. In all cases, the standard rule advised continuing to the second stage whereas the multinomial rule advised stopping early because of excessive numbers of patients experiencing early disease progression. Final trial results indicated that the multinomial recommendation was appropriate, because in no study did final results lead to conclusions of activity. CONCLUSION: In this series of trials, the multinomial stopping rule performed more efficiently than the Fleming or Gehan rules in advising early stopping of trials. These results encourage continued exploration of this approach for phase II trials of cytotoxic and noncytotoxic anticancer agents.

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Lam, Kin-Ho, Delyar Tabatabai, Jed Irvine, Donald Bertucci, Anita Ruangrotsakun, Minsuk Kahng, and Alan Fern. "Beyond Value: CheckList for Testing Inferences in Planning-Based RL." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 606–14. http://dx.doi.org/10.1609/icaps.v32i1.19848.

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Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited evidence for post-deployment generalization beyond the test distribution. In this paper, we address this limitation by extending the recent CheckList testing methodology from natural language processing to planning-based RL. Specifically, we consider testing RL agents that make decisions via online tree search using a learned transition model and value function. The key idea is to improve the assessment of future performance via a CheckList approach for exploring and assessing the agent's inferences during tree search. The approach provides the user with an interface and general query-rule mechanism for identifying potential inference flaws and validating expected inference invariances. We present a user study involving knowledgeable AI researchers using the approach to evaluate an agent trained to play a complex real-time strategy game. The results show the approach is effective in allowing users to identify previously-unknown flaws in the agent's reasoning. In addition, our analysis provides insight into how AI experts use this type of testing approach, which may help improve future instantiations.

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Bewley, Tom, and Jonathan Lawry. "TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11415–22. http://dx.doi.org/10.1609/aaai.v35i13.17360.

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In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep reinforcement learning, are currently so lacking in interpretable structure as to effectively be black boxes, but insights may still be gained from an external, behaviourist perspective. Inspired by conceptual spaces theory, we suggest that a versatile first step towards general understanding is to discretise the state space into convex regions, jointly capturing similarities over the agent's action, value function and temporal dynamics within a dataset of observations. We create such a representation using a novel variant of the CART decision tree algorithm, and demonstrate how it facilitates practical understanding of black box agents through prediction, visualisation and rule-based explanation.

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Zhang, Han, Yutong Li, Jiaoyang Li, T. K. Satish Kumar, and Sven Koenig. "Mutex Propagation in Multi-Agent Path Finding for Large Agents." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 249–53. http://dx.doi.org/10.1609/socs.v15i1.21776.

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Mutex propagation and its concomitant symmetry-breaking techniques have proven useful in Multi-Agent Path Finding (MAPF) with point agents. In this paper, we show that they can be easily generalized to richer MAPF problems. In particular, we demonstrate their application to MAPF with ``Large'' Agents (LA-MAPF). Here, agents can occupy multiple points at the same time according to their fixed shapes and sizes. While existing rule-based symmetry-breaking techniques are difficult to generalize from point agents to large agents, mutex-based symmetry-breaking techniques can be generalized easily. In a Conflict-Based Search (CBS) framework for LA-MAPF, we also develop a mutex-based conflict-selection strategy to further enhance the efficiency of the search. Through experiments on various maps, we show that our techniques significantly improve MC-CBS, a state-of-the-art optimal LA-MAPF algorithm, in terms of both success rate and runtime.

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Welk, Amber, Friedrich Recknagel, Hongqing Cao, Wai-Sum Chan, and Anita Talib. "Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms." Ecological Informatics 3, no. 1 (January 2008): 46–54. http://dx.doi.org/10.1016/j.ecoinf.2007.12.002.

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Wickramage, Heshani, David Roberts, and Robert Hearne. "Water Allocation Using the Bankruptcy Model: A Case Study of the Missouri River." Water 12, no. 3 (February 25, 2020): 619. http://dx.doi.org/10.3390/w12030619.

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This research applies cooperative game theory—specifically, the bankruptcy model—to address conflicts arising from the scarcity of water resources shared by multiple agents. This case study addresses potential outcomes of five allocation rules applied to the apportionment of water between two agents in the Missouri River. Currently, there is no interstate compact to apportion Missouri River and frequent disputes between upstream and downstream states occur. Upstream states favor managing reservoir water levels to support reservoir recreation and downstream states want water for the downstream navigation channel. The five allocation rules studied are (1) Proportional Sharing, (2) Constrained Equal Awards, (3) Constrained Equal Losses, (4) Sequential Sharing based on Proportional Sharing, and (5) a proposed Modified Constrained Equal Awards rule. The results of the analysis of apportionment during four dry years in the 2000s show that the best approaches are the proposed Modified Constrained Equal Awards Rule and the Proportional Sharing Rule.

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He, Yan Li, Wei Ping He, Hai Cheng Yang, Ying Zhang, and Kai Zhao. "Knowledge Based Process Scheduling and Management for Multi-Agent Distributed Collaborative Manufacturing." Materials Science Forum 532-533 (December 2006): 1116–19. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.1116.

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Agent technology is widely employed to construct dynamic, distributed collaborative manufacturing systems in which the individual participating agents contribute to execute different part of the whole manufacturing job, and the final goal is achieved through cooperation. However, the cooperation among agents is based on message exchange, and the system requires additional process control and management mechanism to ensure the operation according to a pre-defined process model. This paper defines the collaborative manufacturing job and related information in domain knowledge model, and applies knowledge based expert system to build software agents for process scheduling and management. The system architecture, components and the implementation methods are discussed. A prototype system is constructed on the basis of JADE agent framework and Jess rule engine and demonstrated with an industry product as a case study.

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Juarez, Ruben, and Michael Wu. "Routing-Proofness in Congestion-Prone Networks." Games 10, no. 2 (April 3, 2019): 17. http://dx.doi.org/10.3390/g10020017.

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We consider the problem of sharing the cost of connecting a large number of atomless agents in a network. The centralized agency elicits the target nodes that agents want to connect, and charges agents based on their demands. We look for a cost-sharing mechanism that satisfies three desirable properties: efficiency which charges agents based on the minimum total cost of connecting them in a network, stand-alone core stability which requires charging agents not more than the cost of connecting by themselves directly, and limit routing-proofness which prevents agents from profitable reporting as several agents connecting from A to C to B instead of A to B. We show that these three properties are not always compatible for any set of cost functions and demands. However, when these properties are compatible, a new egalitarian mechanism is shown to satisfy them. When the properties are not compatible, we find a rule that meets stand-alone core stability, limit routing-proofness and minimizes the budget deficit.

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CHAIB-DRAA, B. "INTERACTION BETWEEN AGENTS IN ROUTINE, FAMILIAR AND UNFAMILIAR SITUATION." International Journal of Cooperative Information Systems 05, no. 01 (March 1996): 1–25. http://dx.doi.org/10.1142/s0218843096000026.

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A framework for designing a Multiagent System (MAS) in which agents are capable of coordinating their activities in routine, familiar, and unfamiliar situations is proposed. This framework is based on the Skills, Rules and Knowledge (S-R-K) taxonomy of Rasmussen. Thus, the proposed framework should allow agents to prefer the lower skill-based and rule-based levels rather than the higher knowledge-based level because it is generally easier to obtain and maintain coordination between agents in routine and familiar situations than in unfamiliar situations. The framework should also support each of the three levels because complex tasks combined with complex interactions require all levels. To permit agents to rely on low levels, a suggestion is developed: agents are provided with social laws so as to guarantee coordination between agents and minimize the need for calling a central coordinator or for engaging in negotiation which requires intense communication. Finally, implementation and experiments demonstrated, on some scenarios of urban traffic, the applicability of major concepts developed in this article.

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Ketelaars, Martijn, Peter Borm, and Marieke Quant. "Decentralization and mutual liability rules." Mathematical Methods of Operations Research 92, no. 3 (July 30, 2020): 577–99. http://dx.doi.org/10.1007/s00186-020-00725-7.

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AbstractThis paper builds on the recent work of Groote Schaarsberg et al. (Math Methods Oper Res 87(3):383–409, 2018) on mutual liability problems. In essence, a mutual liability problem comprises a financial network in which agents may have both monetary individual assets and mutual liabilities. Here, mutual liabilities reflect rightful monetary obligations from past bilateral transactions. To settle these liabilities by reallocating the individual assets, mutual liability rules are analyzed that are based on centralized bilateral transfer schemes which use a certain bankruptcy rule as its leading allocation mechanism. In this paper we derive a new characterization of mutual liability rules by taking a decentralized approach instead, which is based on a recursive individual settlement procedure. We show that for bankruptcy rules that satisfy composition, this decentralized procedure always leads to the same allocation as the one prescribed by the corresponding mutual liability rule based on centralized bilateral transfer schemes. Finally, we introduce a new reduction method for mutual liability problems and prove that any bankruptcy-rule-based mutual liability rule is invariant with respect to such a reduction.

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NIZAMUTDINOV, M. M., and V. V. ORESHNIKOV. "MODELING OF ADAPTIVE BEHAVIOR PROPERTIES OF PARTICIPANTS OF SOCIO-ECONOMIC PROCESSES." Izvestia Ufimskogo Nauchnogo Tsentra RAN, no. 3 (September 30, 2021): 55–60. http://dx.doi.org/10.31040/2222-8349-2021-0-3-55-60.

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The article deals with the issues of modeling socio-economic systems at the regional level. A promising direction in this area is the use of an agent-based approach based on taking into account the features and a certain autonomy of the behavior of interacting agents. Moreover, one of their key properties is the ability to adapt to changing conditions of functioning. Proceeding from this, in order to solve the problems of increasing the efficiency of developing strategic decisions, it is necessary to develop tools to support management decisions, which will be based on the use of an adaptive simulation model of the region. To do this, the article provides a brief overview of the types of agents depending on the methods of processing and using the information they perceive, an algorithm for the functioning of the agent within the framework of the simulation model is presented. It is shown that the information received by the agent can be of different nature. Separate characteristics of any objects, phenomena form a database. The collection of such information forms a precedent. A set of precedents form a rule base. The analysis showed that the adaptability of the agent's behavior is becoming an essential condition for the implementation of his goals. It should be noted that the agent acts in conditions of incomplete information, and his actions are largely described by the theory of bounded rationality. The structure of perceived information is shown and an approach to reflecting the adaptive properties of an agent through the learning subsystem is proposed. Its elements and implementation options are highlighted. From the point of view of reflecting the adaptive properties of the agent, obtaining new information leads to the replenishment of the database and the base of use cases. At the same time, within the framework of analytical activities, new information is compared with the expectations formed on the basis of previously available data and reflected in the agent's rule base. If the information received is contrary to the existing rules, then this becomes the starting point for their revision. Thus, the adaptation of the agent within the framework of the developed simulation model of the region is based on the adjustment of the rule base.

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Zhong, Shiquan, Ning Jia, and Shoufeng Ma. "Iterated snowdrift game among mobile agents with myopic expected-reward based decision rule: Numerical and analytical research." Physica A: Statistical Mechanics and its Applications 414 (November 2014): 6–18. http://dx.doi.org/10.1016/j.physa.2014.06.080.

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Misirli, Goksel, Matteo Cavaliere, William Waites, Matthew Pocock, Curtis Madsen, Owen Gilfellon, Ricardo Honorato-Zimmer, Paolo Zuliani, Vincent Danos, and Anil Wipat. "Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization." Bioinformatics 32, no. 6 (November 11, 2015): 908–17. http://dx.doi.org/10.1093/bioinformatics/btv660.

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Abstract Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk

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Medill, Colleen. "The Federal Common Law of Vicarious Fiduciary Liability under ERISA." University of Michigan Journal of Law Reform, no. 44.2 (2011): 249. http://dx.doi.org/10.36646/mjlr.44.2.federal.

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The Employee Retirement Income Security Act of 1974 ("ERISA"), the federal law that regulates employer-sponsored benefit plans, has a rich history of judiciallycreated federal common law. This Article explores the theoretical, policy, statutory, and stare decisis grounds for the development of another area offederal common law under ERISA-the incorporation of respondeat superior liability principles to impose ERISA fiduciary liability ("vicarious fiduciary liability") upon a corporation for the fiduciary activities of its employees or agents. The Article proposes that the federal courts should adopt a federal common law rule of vicarious fiduciary liability under ERISA based on the traditional scope of employment approach. Under such a rule, a corporate principal whose own internal employees or agents perform fiduciary functions during the course and within the scope of their employment or agency relationship would be strictly liable under ERISA for any breach of fiduciary duty by the employee or agent. Vicarious fiduciary liability should be limited, however, so that a nonfiduciary corporate principal would not be subject to damages claims under ERISA for the rogue fiduciary activities of its employees or agents, but would be subject to restitution as necessary to prevent unjust enrichment of the principal. A federal common law rule of vicarious fiduciary liability under ERISA is necessary for two reasons. First, a rule of vicarious fiduciary liability is essential to maintaining and enforcing ERISA's comprehensive system of fiduciary regulation. Second, vicarious fiduciary liability is needed to prevent employer overreaching under the judicially-created settlor function defense to breach of fiduciary duty claims. Absent a federal common law rule of vicarious fiduciary liability, a corporate employer in its nonfiduciary capacity as the settlor of its ERISA plan, may design the documents that govern the employer's plan as a shield against fiduciary responsibility for the actions of the employer's own internal fiduciary employees. This misuse of nonfiduciary settlor powers, which is contrary to both the letter and the spirit of ERISA, would be prevented by a federal common law rule of vicarious fiduciary liability.

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Agirbas, Asli. "Optimization test of a rule-based swarm intelligence simulation for the conceptual design process." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 4 (July 15, 2020): 477–91. http://dx.doi.org/10.1017/s0890060420000323.

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AbstractToday, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

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CARDOSO, HENRIQUE LOPES, and EUGÉNIO OLIVEIRA. "INSTITUTIONAL REALITY AND NORMS: SPECIFYING AND MONITORING AGENT ORGANIZATIONS." International Journal of Cooperative Information Systems 16, no. 01 (March 2007): 67–95. http://dx.doi.org/10.1142/s0218843007001573.

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Norms and institutions have been proposed to regulate multi-agent interactions. However, agents are intrinsically autonomous, and may thus decide whether to comply with norms. On the other hand, besides institutional norms, agents may adopt new norms by establishing commitments with other agents. In this paper, we address these issues by considering an electronic institution that monitors the compliance to norms in an evolving normative framework: norms are used both to regulate an existing environment and to define contracts that make agents' commitments explicit. In particular, we consider the creation of virtual organizations in which agents commit to certain cooperation efforts regulated by appropriate norms. The supervision of norm fulfillment is based on the notion of institutional reality, which is constructed by assigning powers to agents enacting institutional roles. Constitutive rules make a connection between the illocutions of those agents and institutional facts, certifying the occurrence of associated external transactions. Contract specification is based on conditional prescription of obligations. Contract monitoring relies on rules for detecting the fulfillment and violation of those obligations. The implementation of our normative institutional environment is supported by a rule-based inference engine.

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Zhang, Wen Guang, Ji Zhen Liu, De Liang Zeng, and Yan Song Deng. "Robust Consensus of Multi-Agent Systems with Nonuniform Time-Delays and Switching Topologies." Applied Mechanics and Materials 336-338 (July 2013): 804–10. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.804.

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This paper studies the robust consensus problem for multi-agent systems with nonuniform time-delays and switching topologies in the presence of external disturbances, where each agent has a self-delay and each delay is independent of each other. A neighbor-based rule is used for the agents with nonuniform time-delays and external disturbances. A sufficient condition is derived under which all agents achieve consensus while satisfying desired H_infinity performance. Finally, the effectiveness of the obtained theoretical results is demonstrated through numerical simulations.

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Wai, Sim Keng, Cheah WaiShiang, Muhammad Asyraf Bin Khairuddin, Yanti Rosmunie Binti Bujang, Rahmat Hidayat, and Celine Haren Paschal. "Autonomous Agents in 3D Crowd Simulation Through BDI Architecture." JOIV : International Journal on Informatics Visualization 5, no. 1 (March 18, 2021): 1. http://dx.doi.org/10.30630/joiv.5.1.371.

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Agent based simulation (ABS) is a paradigm to modelling systems included of autonomous and interacting agents. ABS has been tremendous growth and used by researchers in the social sciences to study socio-environmental complex systems. To date, various platforms have been introduced for agent-based social simulation. They are rule based in any logic, python based in SPADE and etc. Although those platforms have been introduced, there is still an insufficient to develop a crowd simulation in 3D platform. Having a 3D platform is needed to enabling the crowd simulation for training purposes. However, the current tools and platform still lack features to develop and simulate autonomous agents in the 3D world. This paper introduced a BDI plug in at Unity3D for crowd simulation. BDI is an intelligent agent architecture and it is able to develop autonomous agents in crowd environment. In this paper, we present the BDI plug with a case study of Australia bush fire and discuss a method to support autonomous agents' development in 3D crowd simulation. The tool allows the modeller to develop autonomous agents in 3D world by taking the advantages of Unity3D.

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Han, Wei, Bing Zhang, Qianyi Wang, Jun Luo, Weizhi Ran, and Yang Xu. "A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles." Applied Sciences 9, no. 6 (March 15, 2019): 1089. http://dx.doi.org/10.3390/app9061089.

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The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making.

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Sueoka, Yuichiro, Takamasa Tahara, Masato Ishikawa, and Koichi Osuka. "Statistical Exploration of Distributed Pattern Formation Based on Minimalistic Approach." Journal of Robotics and Mechatronics 31, no. 6 (December 20, 2019): 905–12. http://dx.doi.org/10.20965/jrm.2019.p0905.

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In this paper, we discuss the pattern formation of objects that can be stacked and transported by distributed autonomous agents. Inspired by the social behavior of termite colonies, which often build elaborate three-dimensional structures (nest towers), this paper explores the mechanism of termite-like agents through a computational and minimalistic approach. We introduce a cellular automata model (i.e., spatially discretized) for the agents and the objects they can transport, where each agent follows a “rule” determined by the assignment of fundamental actions (move/ load/ unload) based on the state of its neighboring cells. To evaluate the resulting patterns from the viewpoint of structural complexity and agent effort, we classify the patterns using the Kolmogorov dimension and higher-order local autocorrelation, two well-known statistical techniques in image processing. We find that the Kolmogorov dimension provides a good metric for the structural complexity of a pattern, whereas the higher-order local autocorrelation is an effective means of identifying particular local patterns.

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Rian Adam Rajagede. "MAINTAIN AGENT CONSISTENCY IN SURAKARTA CHESS USING DUELING DEEP NETWORK WITH INCREASING BATCH." IIUM Engineering Journal 23, no. 1 (January 4, 2022): 159–71. http://dx.doi.org/10.31436/iiumej.v23i1.1807.

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Deep reinforcement learning usage in creating intelligent agents for various tasks has shown outstanding performance, particularly the Q-Learning algorithm. Deep Q-Network (DQN) is a reinforcement learning algorithm that combines the Q-Learning algorithm and deep neural networks as an approximator function. In the single-agent environment, the DQN model successfully surpasses human ability several times over. Still, when there are other agents in the environment, DQN may experience decreased performance. This research evaluated a DQN agent to play in the two-player traditional board game of Surakarta Chess. One of the drawbacks that we found when using DQN in two-player games is its consistency. The agent will experience performance degradation when facing different opponents. This research shows Dueling Deep Q-Network usage with increasing batch size can improve the agent's performance consistency. Our agent trained against a rule-based agent that acts based on the Surakarta Chess positional properties and was then evaluated using different rule-based agents. The best agent used Dueling DQN architecture with increasing batch size that produced a 57% average win rate against ten different agents after training for a short period. ABSTRAK: Pembelajaran Peneguhan Mendalam adalah terbaik apabila digunakan bagi mewujudkan ejen pintar dalam menyelesaikan pelbagai tugasan, terutama jika ia melibatkan algoritma Pembelajaran-Q. Algoritma Rangkaian-Q Mendalam (DQN) adalah Pembelajaran Peneguhan berasaskan gabungan algoritma Pembelajaran-Q dan rangkaian neural sebagai fungsi penghampiran. Melalui persekitaran ejen tunggal, model DQN telah beberapa kali berjaya mengatasi kemampuan manusia. Namun, ketika ejen lain berada dalam persekitaran ini, DQN mungkin kurang berjaya. Kajian ini melibatkan ejen DQN bermain papan tradisional iaitu Catur Surakarta dengan dua pemain. Salah satu kekurangan yang dijumpai adalah konsistensi. Ejen ini akan kurang bagus ketika berhadapan lawan berbeza. Kajian menunjukkan dengan penggunaan Rangkaian-Q Dwipertarungan Mendalam bersama peningkatan saiz kumpulan dapat meningkatkan konsistensi prestasi ejen. Ejen ini telah dilatih untuk melawan ejen lain berasaskan peraturan dan sifat kedudukan Catur Surakarta. Kemudian, ejen ini diuji berpandukan peraturan berbeza. Ejen terbaik adalah yang menggunakan rekaan DQN Dwipertarungan bersama peningkatan saiz kumpulan. Ianya berhasil memenangi permainan dengan purata 57% berbanding sepuluh agen lain melalui latihan jangka masa pendek.

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Perez-Liebana, Diego, Yu-Jhen Hsu, Stavros Emmanouilidis, Bobby Khaleque, and Raluca Gaina. "Tribes: A New Turn-Based Strategy Game for AI Research." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 252–58. http://dx.doi.org/10.1609/aiide.v16i1.7438.

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This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.

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Georgieva, Maya, Diana Tzankova, Stanislava Vladimirova, and Atanas Bijev. "EVALUATION OF A GROUP OF PYRROLE DERIVATIVES AS TUBERCULOSTATIC AGENTS." CBU International Conference Proceedings 5 (September 24, 2017): 1083–91. http://dx.doi.org/10.12955/cbup.v5.1075.

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: This research aims to contribute to the global search for more effective tuberculostatics that has been triggered by recent outbreaks of tuberculosis. A group of pyrrole-containing derivatives are designed and theoretically elucidated. The identification of the pharmacophore group using the PharmaGist webserver is attempted. Also, the corresponding drug-like properties of the tested compounds are evaluated, together with their possible toxicity risks. The pharmacokinetic behavior of the structures is predicted, based on the Lipinskiâ€™s Rule of Five. The effects of some structural parameters are tested. In addition, in vitro evaluations of the anti-tubercular activity against Mycobacterium tuberculosis H37Rv are performed, with compound GA-9 registering the highest activity.

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Jalasvuori, Matti, Maija P. Jalasvuori, and Jaana K. H. Bamford. "Dynamics of a Laterally Evolving Community of Ribozyme-Like Agents as Studied with a Rule-Based Computing System." Origins of Life and Evolution of Biospheres 40, no. 3 (December 12, 2009): 319–34. http://dx.doi.org/10.1007/s11084-009-9187-0.

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Journal articles on the topic 'Rule-based agents'

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