Готові списки джерел за темами / Agents behaviour

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  2. Дисертації
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Добірка наукової літератури з теми "Agents behaviour"

Автор: Grafiati
Опубліковано: 14 вересня 2024

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Статті в журналах з теми "Agents behaviour"

1

Sitanskiy, Stanislav, Laura Sebastia, and Eva Onaindia. "Behaviour recognition of planning agents using Behaviour Trees." Procedia Computer Science 176 (2020): 878–87. http://dx.doi.org/10.1016/j.procs.2020.09.083.

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2

Lunze, Jan. "Transient Behaviour of Synchronised Agents." at - Automatisierungstechnik 60, no. 7 (July 2012): 398–404. http://dx.doi.org/10.1524/auto.2012.1012.

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3

Ab Aziz, Azizi, and Mohamad Farif Jemili. "Conceptual Design of a Socially Intelligent Agent with Triadic Empathy and Theory of Mind for Mental Health Support." Journal of Human Centered Technology 1, no. 1 (February 6, 2022): 23–33. http://dx.doi.org/10.11113/humentech.v1n1.12.

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Анотація:
For socially intelligent agents to become a fully digital therapist to support individual with mental health problem in the future, they need to know how to socially interact with humans. One of the key ingredients to allow this skill to take place is an ability to exhibit empathic behaviours. Despite a number of socially intelligent agents were build with empathic behaviour, they only cover single empathy behaviour, contrary to more complex triadic empathic behaviours. In this article, the conceptual design and model to implement triadic empathy in socially intelligent agents is presented.
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4

Zeghache, Linda, Nadjib Badache, Michel Hurfin, and Izabela Moise. "Reliable mobile agents with transactional behaviour." International Journal of Communication Networks and Distributed Systems 13, no. 1 (2014): 56. http://dx.doi.org/10.1504/ijcnds.2014.063977.

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5

Smith, J. Barry, and Shlomo Weber. "Rent-seeking behaviour of retaliating agents." Public Choice 61, no. 2 (May 1989): 153–66. http://dx.doi.org/10.1007/bf00115661.

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6

Johnson, Susan C. "Detecting agents." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1431 (February 21, 2003): 549–59. http://dx.doi.org/10.1098/rstb.2002.1237.

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Анотація:
This paper reviews a recent set of behavioural studies that examine the scope and nature of the representational system underlying theory–of–mind development. Studies with typically developing infants, adults and children with autism all converge on the claim that there is a specialized input system that uses not only morphological cues, but also behavioural cues to categorize novel objects as agents. Evidence is reviewed in which 12– to 15–month–old infants treat certain non–human objects as if they have perceptual/attentional abilities, communicative abilities and goal–directed behaviour. They will follow the attentional orientation of an amorphously shaped novel object if it interacts contingently with them or with another person. They also seem to use a novel object's environmentally directed behaviour to determine its perceptual/attentional orientation and object–oriented goals. Results from adults and children with autism are strikingly similar, despite adults' contradictory beliefs about the objects in question and the failure of children with autism to ultimately develop more advanced theory–of–mind reasoning. The implications for a general theory–of–mind development are discussed.
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7

GOYAL, MADHU. "ATTITUDE CYCLE FOR PROBLEM SOLVING TEAMS IN A DYNAMIC WORLD." International Journal on Artificial Intelligence Tools 13, no. 04 (December 2004): 945–59. http://dx.doi.org/10.1142/s0218213004001910.

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Анотація:
In this paper, a mental attribute called attitude is introduced and its importance in agent problem solving is discussed. It presents the various properties of agents describing how the attitudes of the agents affect the behavior of the agents. The paper also discusses how the attitudes could be described computationally in terms of various attributes. This paper formalizes the team as a collective abstract attitude of participating agents. This concept especially has been very useful in formalising the behaviour of complex teams. The team model grounds the team attitude as the individual attitude of its member agents, which in turn is further divided into the attitudes and behaviours towards the various team attributes. In this paper a team problem solving methodology is also presented, which has the notion attitude and team cycle as its core to allow robust and coherent team behavior. It also shows how these various attitudes ultimately result into various team behaviors in a fire world. The application and implementation of this methodology to a virtual fire-fighting domain has revealed a promising prospect in developing problem solving team agents.
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8

Stoecker, Ralf. "Agents in Action." Grazer Philosophische Studien 61, no. 1 (June 1, 2001): 21–42. http://dx.doi.org/10.1163/18756735-061001004.

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Анотація:
I offer a justification for the received view that the characteristic feature of agents is to be found in the particular way their behaviour is explainable. Agents are people who have acquired three skills: (i) to act in accordance with inner or public deliberation; (ii) to do many things almost as if they had deliberated; and (iii) to recognize situations where it is worthwhile to switch from the second to the first skill. We can therefore assume that agents behave as if they were accompanying their behaviour by constant thinking although they don't actually deliberate all the time. This view allows for some attractive solutions for notorious problems in action theory but has the surprising ontological consequence that, although there are agents in action, there are no actions.
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9

ShreeJain, Bhagya, Sagar Chandrakar, and Shrikant Tiwari. "Steering Behaviour for Computer based Intelligent Agents." International Journal of Computer Applications 89, no. 13 (March 26, 2014): 38–41. http://dx.doi.org/10.5120/15694-4580.

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10

Uhlendorf, Volkmar, Frank-Detlef Scholle, and Michael Reinhardt. "Acoustic behaviour of current ultrasound contrast agents." Ultrasonics 38, no. 1-8 (March 2000): 81–86. http://dx.doi.org/10.1016/s0041-624x(99)00128-6.

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Дисертації з теми "Agents behaviour"

1

Egginton, Robert. "Predicting and Learning the Behaviour of Intelligent Agents." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521100.

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2

Walker, Tom. "Moral agents : our inbuilt capacity for unselfish behaviour." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422644.

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3

Shearer, John. "Persuasive interactive non-verbal behaviour in embodied conversational agents." Thesis, Newcastle upon Tyne : University of Newcastle upon Tyne, 2009. http://hdl.handle.net/10443/791.

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4

Kamyab, Tehrani Kaveh Richard. "Effective delivery of believable behaviour for embodied conversational agents." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/8286.

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5

Jaafar, Jafreezal. "Reactive behaviour for autonomous virtual agents using fuzzy logic." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/8317.

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Анотація:
One of the fundamental aspects of a virtual environment is the virtual agents that inhabit them. In many applications, virtual agents are required to perceive input information from their environment and make decisions appropriate to their task based on their programmed reaction to those inputs. The research presented in this thesis focuses on the reactive behaviour of the agents. We propose a new control architecture to allow agents to behave autonomously in navigation tasks in unknown environments. Our behaviour-based architecture uses fuzzy logic to solve problems of agent control and action selection and which can coordinate conflicts among different operations of reactive behaviours. A Fuzzy Associative Memory (FAM) is used as the process of encoding and mapping the input fuzzy sets to the output fuzzy set and to optimise the fuzzy rules. Our action selection algorithm is based on the fuzzy α-level method with the Hurwicz criterion. The main objective of the thesis was to implement agent navigation from point to point by a coordination of planning, sensing and control. However, we believe that the reactive architecture emerging from this research is sufficiently general that it could be applied to many applications in widely differing domains where real-time decision making under uncertainty is required. To illustrate this generality, we show how the architecture is applied to a different domain. We chose the example of a computer game since it clearly demonstrates the attributes of our architecture: real-time action selection and handling uncertainty. Experimental results are presented for both implementations which show how the fuzzy method is applied, its generality and that it is robust enough to handle different uncertainties in different environments. In summary, the proposed reactive architecture is shown to solve aspects of behaviour control for autonomous virtual agents in virtual environments and can be applied to various application domains.
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6

Allam, Hossam. "Modelling learning behaviour of intelligent agents using UML 2.0." Thesis, University of Plymouth, 2005. http://hdl.handle.net/10026.1/338.

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Анотація:
This thesis aims to explore and demonstrate the ability of the new standard of structural and behavioural components in Unified Modelling Language (UML 2.0 / 2004) to model the learning behaviour of Intelligent Agents. The thesis adopts the research direction that views agent-oriented systems as an extension to object-oriented systems. In view of the fact that UML has been the de facto standard for modelling object-oriented systems, this thesis concentrates on exploring such modelling potential with Intelligent Agent-oriented systems. Intelligent Agents are Agents that have the capability to learn and reach agreement with other Agents or users. The research focuses on modelling the learning behaviour of a single Intelligent Agent, as it is the core of multi-agent systems. During the writing of the thesis, the only work done to use UML 2.0 to model structural components of Agents was from the Foundation for Intelligent Physical Agent (FIPA). The research builds upon, explores, and utilises this work and provides further development to model the structural components of learning behaviour of Intelligent Agents. The research also shows the ability of UML version 2.0 behaviour diagrams, namely activity diagrams and sequence diagrams, to model the learning behaviour of Intelligent Agents that use learning from observation and discovery as well as learning from examples of strategies. The research also evaluates if UML 2.0 state machine diagrams can model specific reinforcement learning algorithms, namely dynamic programming, Monte Carlo, and temporal difference algorithms. The thesis includes user guides of UML 2.0 activity, sequence, and state machine diagrams to allow researchers in agent-oriented systems to use the UML 2.0 diagrams in modelling the learning components of Intelligent Agents. The capacity for learning is a crucial feature of Intelligent Agents. The research identifies different learning components required to model the learning behaviour of Intelligent Agents such as learning goals, learning strategies, and learning feedback methods. In recent years, the Agent-oriented research has been geared towards the agency dimension of Intelligent Agents. Thus, there is a need to conduct more research on the intelligence dimension of Intelligent Agents, such as negotiation and argumentation skills. The research shows that behavioural components of UML 2.0 are capable of modelling the learning behaviour of Intelligent Agents while structural components of UML 2.0 need extension to cover structural requirements of Agents and Intelligent Agents. UML 2.0 has an extension mechanism to fulfil Agents and Intelligent Agents for such requirements. This thesis will lead to increasing interest in the intelligence dimension rather than the agency dimension of Intelligent Agents, and pave the way for objectoriented methodologies to shift more easily to paradigms of Intelligent Agent-oriented systems.
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7

Gilani, Syed Zafar ul Hussan. "Understanding the behaviour and influence of automated social agents." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/279022.

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Анотація:
Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.
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8

López, y. López Fabiola. "Social power and norms : impact on agent behaviour." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273756.

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9

Chin, Chien Ting. "Modelling the behaviour of microbubble contrast agents for diagnostic ultrasound." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ59006.pdf.

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Wates, Julia M. "Solution behaviour of cationic surfactants relevant to industrial applications." Thesis, University of Salford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.258424.

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Книги з теми "Agents behaviour"

1

Aghajan, Hamid K., and Björn Gottfried. Behaviour monitoring and interpretation: BMI : well-being. Amsterdam, The Netherlands: IOS Press, 2011.

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2

1943-, Joseph M. H., ed. Monitoring neurotransmitter release during behaviour. Weinheim, Federal Republic of Germany: VCH, 1986.

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3

Björn, Gottfried, and Aghajan Hamid K, eds. Behaviour monitoring and interpretation - BMI: Smart environments. Amsterdam: IOS Press, 2009.

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4

Chan, Chris Chi-Yet. Behaviour of metals in MSW incinerator fly ash during roasting with chlorinating agents. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1997.

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5

Filipowicz, Judy Rachel. Using ART 1 (Adaptive Resonance Theory 1) to Study Flocking Behaviour in Intelligent agents. Oxford: Oxford Brookes University, 2001.

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6

C, Billari Francesco, Prskawetz Alexia, and Workshop on Agent-Based Computational Demography (2001 : Rostock, Germany), eds. Agent-based computational demography: Using simulation to improve our understanding of demographic behaviour. New York: Springer, 2003.

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7

Gupta, Indrani. Treatment-seeking behaviour and the willingness to pay for antiretroviral therapy of HIV positive patients in India. Delhi: Institute of Economic Growth, 2003.

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8

Corkum, Mary Jane. Consequences of social stress and the influence of flavouring agents on ingestive behaviour of feedlot steers. Charlottetown: University of Prince Edward Island, 1992.

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9

Vellakkal, Sukumar. Adverse selection and private health insurance coverage in India: A rational behaviour model of insurance agents under asymmetric information. New Delhi: Indian Council for Research on International Economic Relations, 2009.

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10

Bordignon, Massimo. An investigation in the theory of voluntary provision of public goods and income tax evasion under the hypothesisof ethical behaviour on the part of economic agents. [s.l.]: typescript, 1989.

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Частини книг з теми "Agents behaviour"

1

Vierlinger, Robert. "Towards AI Drawing Agents." In Modelling Behaviour, 357–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24208-8_30.

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Penders, Jacques. "Conflict-based Behaviour Emergence in Robot Teams." In Conflicting Agents, 169–202. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/0-306-46985-5_6.

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3

Bevacqua, Elisabetta, Maurizio Mancini, and Catherine Pelachaud. "A Listening Agent Exhibiting Variable Behaviour." In Intelligent Virtual Agents, 262–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85483-8_27.

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4

Hussaini, Naziya, and Ruth Aylett. "Modelling Group Behaviour in Autonomous Synthetic Characters." In Intelligent Virtual Agents, 128–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21996-7_14.

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5

Coutinho, Eduardo, Eduardo Reck Miranda, and Patricio da Silva. "Evolving Emotional Behaviour for Expressive Performance of Music." In Intelligent Virtual Agents, 497. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550617_48.

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6

Lemmens, Nyree, Steven de Jong, Karl Tuyls, and Ann Nowé. "Bee Behaviour in Multi-agent Systems." In Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, 145–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77949-0_11.

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7

Lindsay, Alan, Fred Charles, Jonathon Read, Julie Porteous, Marc Cavazza, and Gersende Georg. "Generation of Non-compliant Behaviour in Virtual Medical Narratives." In Intelligent Virtual Agents, 216–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21996-7_22.

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8

Grimsley, Michael, and Anthony Meehan. "Measuring Behaviour-Based Trust between Negotiating Agents." In Cooperative Information Agents VI, 112–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45741-0_11.

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Brazier, Frances, Barbara Dunin-Keplicz, Jan Treur, and Rineke Verbrugge. "Modelling Internal Dynamic Behaviour of BDI Agents." In Formal Models of Agents, 36–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46581-2_4.

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Hussaini, Naziya, and Ruth Aylett. "A Step towards Modelling Group Behaviour in Autonomous Synthetic Characters." In Intelligent Virtual Agents, 214–17. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09767-1_26.

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Тези доповідей конференцій з теми "Agents behaviour"

1

Jahl, Alexander, Harun Baraki, Stefan Jakob, Malte Fax, and Kurt Geihs. "Machine-learned Behaviour Models for a Distributed Behaviour Repository." In 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010804000003116.

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2

"Characterising behaviour of human agents." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.g4.smajgl.

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3

Hegde, Aditya, Vibhav Agarwal, and Shrisha Rao. "Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/24.

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Анотація:
Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them with those of virtue agents.
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4

Ojeniyi, Adegoke, Azizi Ab Aziz, and Yuhanis Yusof. "Verification analysis of an agent based model in behaviour change process." In 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR). IEEE, 2015. http://dx.doi.org/10.1109/isamsr.2015.7379776.

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Arafa, Yasmine, and Abe Mamdani. "Scripting embodied agents behaviour with CML." In the 8th international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/604045.604109.

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Mulvana, Helen, Robert J. Eckersley, and Eleanor Stride. "Temperature behaviour of ultrasound contrast agents." In 2009 IEEE International Ultrasonics Symposium. IEEE, 2009. http://dx.doi.org/10.1109/ultsym.2009.5441522.

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7

Duan, Wei, Jie Lu, and Junyu Xuan. "Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/434.

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Анотація:
Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behaviour similarities within groups, they commonly fall short in concurrently learning the latent graph, thereby constraining the information exchange among partially observed agents. To overcome these limitations, we present a novel approach to infer the Group-Aware Coordination Graph (GACG), which is designed to capture both the cooperation between agent pairs based on current observations and group-level dependencies from behaviour patterns observed across trajectories. This graph is further used in graph convolution for information exchange between agents during decision-making. To further ensure behavioural consistency among agents within the same group, we introduce a group distance loss, which promotes group cohesion and encourages specialization between groups. Our evaluations, conducted on StarCraft II micromanagement tasks, demonstrate GACG's superior performance. An ablation study further provides experimental evidence of the effectiveness of each component of our method.
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"Human Behaviour Modelling for Games using Agents." In International Conference on Advances in Engineering and Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0314005.

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"AGENT-BASED COMPUTER-GENERATED-FORCES’ BEHAVIOUR IMPROVEMENT." In 3rd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003187002730280.

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Butler, Ryan, and Edwin Simpson. "Analysing Customer Behaviour Using Simulated Transactional Data." In 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011902100003393.

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Звіти організацій з теми "Agents behaviour"

1

Noah, Alphonse, and Ruth Tacneng. Cameroon’s Tax on Mobile Money: Implications for Agents' Performance and Revenue Sustainability. Institute of Development Studies, May 2024. http://dx.doi.org/10.19088/ictd.2024.035.

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Анотація:
Mobile money taxation gives African governments an opportunity to broaden their fiscal base and explore new revenue-generating possibilities. Cameroon introduced a 0.2 per cent tax on mobile money transfers and withdrawals from 1 January 2022. Our research analyses the behaviour of agents, who act as intermediaries between mobile money account holders and mobile money service providers, before and after the tax on mobile money (MM tax). Agents play a key role in the distribution of mobile money services. Their presence is vital for achieving financial inclusion, especially in areas less served by banks and other traditional financial service providers. An agent’s revenue is mainly derived from commission earned on each transaction – they receive an average of 40–45 per cent of the commission, and the remaining 55–60 per cent is shared between the mobile network operator, partner banks, and agent’s manager (superagent). Given their importance in the mobile money ecosystem, factors that negatively affect the attractiveness of the business for agents could have policy implications on financial inclusion. Summary of ICTD Working Paper 192.
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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.
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De Carlo, E. H. Chemical behaviour of geothermal silica after precipitation from geothermal fluids with inorganic flocculating agents at the Hawaii Geothermal Project Well-A (HGP-A). Office of Scientific and Technical Information (OSTI), January 1987. http://dx.doi.org/10.2172/6784371.

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Abdul Rahim, Nor Azura, Noor Syazril Jafri, and Muna'amirah Mohamad. DEVELOPMENT OF OIL ABSORBENT MAT FROM WASTE NBR GLOVES. Penerbit Universiti Malaysia Perlis, 2023. http://dx.doi.org/10.58915/techrpt2023.003.

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Waste NBR rubber gloves have become an environmental burden because the chemically cross-linked NBR gloves can’t be recycled. In this particular research, an initiative was made to turn the NBR waste glove into an oil absorbent mat by using the amount of chemical cross-linking inside the NBR waste glove triggered by solvents and with the help of epoxy resin as a binding agent. The absorption test used two types of oil: cooking oil and engine oil. To justify the relationship between the developed oil absorbent mat and its absorption behaviour, various tests, namely the Fourier transform infrared radiation (FTIR), scanning electron microscopy (SEM), and absorbency test, were conducted. Epoxy resin was used as a binding agent in the production of oil-absorbing mats made from waste NBR gloves. The NBR gloves mixed with binding agents of epoxy resin mats have proven to be an effective medium for oil absorbance purposes.
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DeLoach, Scott A. Specifying Agent Behavior as Concurrent Tasks: Defining the Behavior of Social Agents. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada380337.

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Rincón-Torres, Andrey Duván, Andrés Felipe Salas-Ávila, and Juan Manuel Julio-Román. Inflation Expectations: Rationality, Disagreement and the Role of the Loss Function in Colombia. Banco de la República, December 2023. http://dx.doi.org/10.32468/be.1262.

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We study the behaviour of three quantitative sample surveys and a non sample inflation expectation report for Colombia. We found that expectations in Colombia; (i) are not strongly, i.e. a la Muth, rational because they show cross-section disagreement, (ii) expectations, however, show some features of weak rationality, (iii) expectations disagreement is time varying and relate to inflation, inflation changes and the output gap, thus suggesting a staggered information flow to agents, (iv) the forecast error loss function employed by agents is not symmetric and increasingly penalizes higher expectations than finally observed inflation as the horizon grows, and (v) this fact also explains the stylised fact that observed expectation share with theoretical rational expectations that expectations look like lagged versions of inflation that dampen with the horizon. The latest finding also arises from a very general econometric set up we develop in this paper. These results imply that the effect of weakening the rational expectations assumption in Colombian monetary policy models should be assessed, especially when compared to sticky information and heterogeneous agents choosing non Mean Square forecast Error losses.
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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), April 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.
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Pérez Montes, Carlos, Alejandro Ferrer, Gabriel Jiménez, Laura Álvarez Román, Henrique Basso, Beatriz González López, Sergio Mayordomo, et al. Individual and sectoral analysis framework for the impact of economic and financial risks. Madrid: Banco de España, November 2023. http://dx.doi.org/10.53479/34812.

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The Banco de España uses various microeconomic models, mostly of an empirical nature, to support its decision-making in relation to the analysis of economic and financial risks and economic policy advice. These models, which complement those of a macroeconomic nature, seeks to identify the potentially heterogeneous impact on different groups of agents of certain economic, financial or public policy scenarios. This analysis covers many areas, including the study of the behaviour of households and non-financial corporations, the internal credit rating of companies, the study of the demand for and supply of bank credit, top-down bank stress tests, supervisory review and evaluation processes (SREP) and the study of non-bank financial intermediaries. This paper shows how these models have been applied to analyse two recent crisis events, the onset of the COVID-19 pandemic and the Russian invasion of Ukraine, illustrating their practical utility and the need for their development and continuous adaptation.
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Zarrieß, Benjamin, and Jens Claßen. On the Decidability of Verifying LTL Properties of Golog Programs. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.200.

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Golog is a high-level action programming language for controlling autonomous agents such as mobile robots. It is defined on top of a logic-based action theory expressed in the Situation Calculus. Before a program is deployed onto an actual robot and executed in the physical world, it is desirable, if not crucial, to verify that it meets certain requirements (typically expressed through temporal formulas) and thus indeed exhibits the desired behaviour. However, due to the high (first-order) expressiveness of the language, the corresponding verification problem is in general undecidable. In this paper, we extend earlier results to identify a large, non-trivial fragment of the formalism where verification is decidable. In particular, we consider properties expressed in a first-order variant of the branching-time temporal logic CTL*. Decidability is obtained by (1) resorting to the decidable first-order fragment C² as underlying base logic, (2) using a fragment of Golog with ground actions only, and (3) requiring the action theory to only admit local effects.
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Loyall, A. B., and Joseph Bates. Behavior-Based Language Generation for Believable Agents,. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada295449.

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