Journal articles: 'Artificial adaptive agents'

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Andreoni, James, and John H. Miller. "Auctions with Artificial Adaptive Agents." Games and Economic Behavior 10, no. 1 (July 1995): 39–64. http://dx.doi.org/10.1006/game.1995.1024.

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Maes, Pattie. "Modeling Adaptive Autonomous Agents." Artificial Life 1, no. 1_2 (October 1993): 135–62. http://dx.doi.org/10.1162/artl.1993.1.1_2.135.

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One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems.

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Wan, Hakman A., and Andrew Hunter. "On Artificial Adaptive Agents Models of Stock Markets." SIMULATION 68, no. 5 (May 1997): 279–89. http://dx.doi.org/10.1177/003754979706800503.

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Steels, Luc. "The Artificial Life Roots of Artificial Intelligence." Artificial Life 1, no. 1_2 (October 1993): 75–110. http://dx.doi.org/10.1162/artl.1993.1.1_2.75.

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Behavior-oriented Artificial Intelligence (AI) is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximizing their own self-preservation in interaction with a dynamically changing environment. The paper addresses this Artificial Life route toward AI and reviews some of the results obtained so far.

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MERTOGUNO, J. SUKARNO. "DISTRIBUTED KNOWLEDGE-BASE: ADAPTIVE MULTI-AGENTS APPROACH." International Journal on Artificial Intelligence Tools 07, no. 01 (March 1998): 59–70. http://dx.doi.org/10.1142/s0218213098000056.

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This paper describes an approach of using Multi-agents theory to construct an adaptive knowledge base system. To represent the knowledge, frame base (graph) knowledge representation has been chosen. The driving force of this study is the intention of having a distributed (possibly across the net) adaptive knowledge base. The challenge of developing an adaptive knowledge base lays on how to evolve the knowledge (adaptability) and how to control the evolution (maintain the quality of the knowledge). In this paper the manifestation of both of the issues above on our model will be addressed.

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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (December 1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.

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Karpov, Valery. "On moral aspects of adaptive behavior of artificial agents." Artificial societies 16, no. 2 (2021): 0. http://dx.doi.org/10.18254/s207751800014740-3.

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This article describes a model of a social agent, whose behavior can be stated in terms of basic moral mechanisms and norms. Morality is considered here as a flexible adaptive mechanism that allows agents to vary behavior depending on the environment conditions. The control system of the social agent is based on the "emotion-requirement" architecture. Together with the mechanisms of imitative behavior and the identification of other observable agents with the subjective "Me" concept, this architecture allows to interpret the agent's behavior in terms of empathy, sympathy, and friend-foe relationships. Experiments with this model are described, the main variable parameter of which was the tendency to sympathy. The objective of the experiments was to determine the dependence of the group "well-being" indicators on their altruism. The results obtained are quite consistent with the well-known sociological conclusions, which made it possible to say that the proposed behavioral models and architecture of agents are adequate to intuitive ideas about the role and essence of morality. Thus, the possibility of transition in this area from abstract humanitarian reasoning to constructive schemes and models of adaptive behavior of artificial agents was demonstrated.

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Pereira, Ademir Rodrigues, and Liu Hsu. "Adaptive Formation Control using Artificial Potentials for Euler-Lagrange Agents." IFAC Proceedings Volumes 41, no. 2 (2008): 10788–93. http://dx.doi.org/10.3182/20080706-5-kr-1001.01829.

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Bullard, James, and John Duffy. "A model of learning and emulation with artificial adaptive agents." Journal of Economic Dynamics and Control 22, no. 2 (February 1998): 179–207. http://dx.doi.org/10.1016/s0165-1889(97)00072-9.

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Vrancx, Peter, Enda Howley, and Matt Knudson. "Preface to the special issue: adaptive learning agents." Knowledge Engineering Review 31, no. 1 (January 2016): 1–2. http://dx.doi.org/10.1017/s0269888915000144.

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Bagnall, Anthony, and Iain Toft. "Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions." Autonomous Agents and Multi-Agent Systems 12, no. 3 (November 14, 2005): 259–92. http://dx.doi.org/10.1007/s10458-005-4948-2.

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Bullard, James, and John Duffy. "LEARNING AND THE STABILITY OF CYCLES." Macroeconomic Dynamics 2, no. 1 (March 1998): 22–48. http://dx.doi.org/10.1017/s1365100598006026.

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We investigate the extent to which agents can learn to coordinate on stationary perfect-foresight cycles in a general-equilibrium environment. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect-foresight dynamics are characterized by steady-state, periodic, or chaotic trajectories for real money balances. We relax the perfect-foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment. These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for prices. The agents' forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption.

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Yu, Chen, Paul Schermerhorn, and Matthias Scheutz. "Adaptive eye gaze patterns in interactions with human and artificial agents." ACM Transactions on Interactive Intelligent Systems 1, no. 2 (January 2012): 1–25. http://dx.doi.org/10.1145/2070719.2070726.

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Duffy, John, and M. Utku Ünver. "Internet auctions with artificial adaptive agents: A study on market design." Journal of Economic Behavior & Organization 67, no. 2 (August 2008): 394–417. http://dx.doi.org/10.1016/j.jebo.2007.03.007.

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Letia, Ioan Alfred, and Octavian Pop. "Towards adaptive normative systems for communities of agents." Web Intelligence and Agent Systems: An International Journal 11, no. 4 (2013): 339–50. http://dx.doi.org/10.3233/wia-130279.

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Yaghmaie, Mahkameh, and Ardeshir Bahreininejad. "A context-aware adaptive learning system using agents." Expert Systems with Applications 38, no. 4 (April 2011): 3280–86. http://dx.doi.org/10.1016/j.eswa.2010.08.113.

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Lipowska, Dorota, and Adam Lipowski. "Naming Game on Adaptive Weighted Networks." Artificial Life 18, no. 3 (July 2012): 311–23. http://dx.doi.org/10.1162/artl_a_00067.

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We examine a naming game on an adaptive weighted network. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. In some cases, depending on the parameters of the model, the preference toward successfully communicating agents is essentially negligible and the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. In some other cases, the preference toward successfully communicating agents becomes much more important and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still present. A comparison of distribution of languages in our model and in the human population is discussed.

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Zhang, Wei, Ziqiang Wu, Yongjie Zhang, and Xiong Xiong. "Adaptive Behavior and Strategy Switching." International Journal of Information Technology & Decision Making 13, no. 03 (May 2014): 567–84. http://dx.doi.org/10.1142/s0219622014500503.

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Using the Agent-based Computational Finance (ACF) method, we build an artificial stock market with heterogeneous adaptive investors and investigate the evolutionary and interacting relationship between rational investors and irrational investors. We find that with strategy switching, there is a symbiosis among the three kinds of investors in the ACF experiments, although the rational investors are often dominant in the market. And our main findings are robust with agents' scale. When the initial values change, the market ecology achieves new equilibrium.

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Lau, Raymond Y. K., Maolin Tang, On Wong, Stephen W. Milliner, and Yi-Ping Phoebe Chen. "An evolutionary learning approach for adaptive negotiation agents." International Journal of Intelligent Systems 21, no. 1 (2005): 41–72. http://dx.doi.org/10.1002/int.20120.

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Benyon, D., and D. Murray. "Adaptive systems: from intelligent tutoring to autonomous agents." Knowledge-Based Systems 6, no. 4 (December 1993): 197–219. http://dx.doi.org/10.1016/0950-7051(93)90012-i.

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Zhang, Huiliang, Xudong Luo, Chunyan Miao, Zhiqi Shen, and Jin You. "Adaptive goal selection for agents in dynamic environments." Knowledge and Information Systems 37, no. 3 (April 18, 2013): 665–92. http://dx.doi.org/10.1007/s10115-013-0645-7.

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Choi, SungJin, MaengSoon Baik, JoonMin Gil, SoonYoung Jung, and ChongSun Hwang. "Adaptive group scheduling mechanism using mobile agents in peer-to-peer grid computing environment." Applied Intelligence 25, no. 2 (October 2006): 199–221. http://dx.doi.org/10.1007/s10489-006-9654-5.

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23

Broekens, Joost, Walter A. Kosters, and Fons J. Verbeek. "Affect, Anticipation, and Adaptation: Affect-Controlled Selection of Anticipatory Simulation in Artificial Adaptive Agents." Adaptive Behavior 15, no. 4 (December 2007): 397–422. http://dx.doi.org/10.1177/1059712307084686.

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BARBER, K. SUZANNE, and CHERYL E. MARTIN. "DYNAMIC ADAPTIVE AUTONOMY IN MULTIAGENT SYSTEMS: REPRESENTATION AND JUSTIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 03 (May 2001): 405–33. http://dx.doi.org/10.1142/s0218001401001015.

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Autonomy is an often cited but rarely agreed upon agent characteristic. Although no definition of agent autonomy is universally accepted, the concept of adaptive autonomy promises increasingly flexible and robust agent-based systems. In general, adaptive autonomy gives agents the ability to seek help for problems or take initiative when otherwise they would be constrained by their design to follow some fixed procedures or rules for interacting with other agents. In order to access these benefits, this article provides a core definition and representation of agent autonomy designed to support the implementation of adaptive agent autonomy. This definition identifies "decision-making control" governing the determination of agent goals and tasks as the key dimension of agent autonomy. In order to gain run-time flexibility and any associated performance improvements, agents must be able to dynamically adapt their autonomy during system operation. This article justifies the implementation of dynamic adaptive autonomy through a series of experiments showing that a multiagent system operating under dynamic adaptive autonomy performs better than a multiagent system operating under fixed autonomy for the same changing run-time conditions.

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Boshy, Shlomy, and Eytan Ruppin. "Evolving Small Neurocontrollers with Self-Organized Compact Encoding." Artificial Life 9, no. 2 (April 2003): 131–51. http://dx.doi.org/10.1162/106454603322221496.

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This article presents a novel method for the evolution of artificial autonomous agents with small neurocontrollers. It is based on adaptive, self-organized compact genotypic encoding (SOCE) generating the phenotypic synaptic weights of the agent's neurocontroller. SOCE implements a parallel evolutionary search for neurocontroller solutions in a dynamically varying and reduced subspace of the original synaptic space. It leads to the emergence of compact successful neurocontrollers starting from large networks. The method can serve to estimate the network size needed to perform a given task, and to delineate the relative importance of the neurons composing the agent's controller network.

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Lee, Yoosook, Travis Collier, C. Taylor, Jason Riggle, and Edward Stabler. "Adaptive communication among collaborative agents: preliminary results with symbol grounding." Artificial Life and Robotics 8, no. 2 (June 2004): 127–32. http://dx.doi.org/10.1007/bf02678880.

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Lee, Yoosook, Travis Collier, C. Taylor, Jason Riggle, and Edward Stabler. "Adaptive communication among collaborative agents: preliminary results with symbol grounding." Artificial Life and Robotics 8, no. 2 (December 2004): 127–32. http://dx.doi.org/10.1007/s10015-004-0299-3.

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Borenstein, Elhanan, and Eytan Ruppin. "The evolution of imitation and mirror neurons in adaptive agents." Cognitive Systems Research 6, no. 3 (September 2005): 229–42. http://dx.doi.org/10.1016/j.cogsys.2004.11.004.

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Tuci, Elio, Christos Ampatzis, Federico Vicentini, and Marco Dorigo. "Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication." Artificial Life 14, no. 2 (April 2008): 157–78. http://dx.doi.org/10.1162/artl.2008.14.2.157.

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This article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group's coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots.

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Huang, Xiaoci, Jianjun Yi, Yang Chen, Xiaomin Zhu, and Zhiyong Dai. "Adaptive agent tracking approach for oil contamination in water environments." International Journal of Advanced Robotic Systems 17, no. 4 (July 1, 2020): 172988142094021. http://dx.doi.org/10.1177/1729881420940217.

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The online monitoring of water environments is urgently needed. A feasible and effective approach is the use of agents. Water environments, similar to other real-world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, agents should be prepared to deal with various situations. In this study, we focused on an adaptive agent tracking approach for oil contamination. An integrated tracking framework, which is used to track the moving contour of oil pollution via a system comprising multiple unmanned surface vehicles, is proposed. The zigzag, unmanned underwater vehicle-gas, cloverleaf trajectory and curvature-weighted deployment algorithm methods are employed with consideration of their suitability to our approach. A cyclic particle swarm optimisation–Kalman method is also proposed. The possible position of moving vertices is predicted by the Kalman filter, and an objective search region is generated around the centre position. Moreover, particle swarm optimisation is performed to search for the best target position in this region. This particle swarm optimisation–Kalman method is circle operated to compensate for the deficiency of a few agents. To evaluate the approach, we conduct usability and performance simulations.

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Gonzalez-Rodriguez, Diego, and Jose Rodolfo Hernandez-Carrion. "Decentralization and heterogeneity in complex adaptive systems." Kybernetes 44, no. 6/7 (June 1, 2015): 1082–93. http://dx.doi.org/10.1108/k-01-2015-0030.

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Purpose – Following a bacterial-based modeling approach, the authors want to model and analyze the impact of both decentralization and heterogeneity on group behavior and collective learning. The paper aims to discuss these issues. Design/methodology/approach – Inspired by bacterial conjugation, the authors have defined an artificial society in which agents’ strategies adapt to changes in resources location, allowing migration, and survival in a dynamic sugarscape-like scenario. To study the impact of these variables the authors have simulated a scenario in which resources are limited and localized. The authors also have defined three constraints in genetic information processing (inhibition of plasmid conjugation, inhibition of plasmid reproduction and inhibition of plasmid mutation). Findings – The results affirmed the hypothesis that efficiency of group adaptation to dynamic environments is better when societies are varied and distributed than when they are homogeneous and centralized. Originality/value – The authors have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing heterogeneity levels and decentralization of communication structures, leading to a global adaptation of the system. This organic approach to model peer-to-peer dynamics in complex adaptive systems (CAS) is what the authors have named “bacterial-based algorithms” because agents exchange strategic information in the same way that bacteria use conjugation and share genome.

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Winikoff, M., and S. Cranefield. "On the Testability of BDI Agent Systems." Journal of Artificial Intelligence Research 51 (September 19, 2014): 71–131. http://dx.doi.org/10.1613/jair.4458.

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Before deploying a software system we need to assure ourselves (and stakeholders) that the system will behave correctly. This assurance is usually done by testing the system. However, it is intuitively obvious that adaptive systems, including agent-based systems, can exhibit complex behaviour, and are thus harder to test. In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents. We analyse the size of the behaviour space of BDI agents and show that although the intuition is correct, the factors that influence the size are not what we expected them to be. Specifically, we found that the introduction of failure handling had a much larger effect on the size of the behaviour space than we expected. We also discuss the implications of these findings on the testability of BDI agents.

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Selim, Kamal Samy, Ahmed Okasha, and Heba M. Ezzat. "Loss Aversion, Adaptive Beliefs, and Asset Pricing Dynamics." Advances in Decision Sciences 2015 (October 8, 2015): 1–18. http://dx.doi.org/10.1155/2015/971269.

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We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.

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Egilmez, Kaan, and Steven H. Kim. "Deployment of robotic agents in uncertain environments: game theoretic rules and simulation studies." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 1 (February 1992): 1–17. http://dx.doi.org/10.1017/s0890060400002912.

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The coordination of intelligent, interacting, agents is rapidly gaining importance as such systems are deployed under diverse conditions. When robots are used in teams rather than as individuals, their coordination can become more critical for system performance than their individual capabilities. The deployment strategies and communication modes play an important role in the coordination of these teams.This paper examines a Game Theoretic deployment approach to robotic teams in an unstructured environment. A simulation model is developed and used to compare the performance of gaming rules with a non-anticipatory deterministic deployment rule. The initial Game Theoretic rule can be enhanced to exhibit both locally and globally adaptive characteristics. The new rule outperforms both the deterministic algorithm and the straightforward game-theoretic rule. This is achieved by adapting to trends in local regions in the environment as well as anticipating global eventualities.

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Cheng, Yujuan, and Hui Yu. "Adaptive Group Consensus of Multi-Agent Networks via Pinning Control." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 05 (April 21, 2016): 1659014. http://dx.doi.org/10.1142/s021800141659014x.

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This paper studies the group consensus problem in networks of multi-agent systems, in which the nonlinear dynamics of all agents are unknown and nonidentical. We assume that the unknown dynamics are linearly parameterized. Adaptive control method is adopted in the algorithm design. A novel algorithm is proposed for the multi-agent systems to reach group consensus via pinning control strategies, which only rely on the relative position information between neighboring agents. The stability and parameter convergence analysis are done based on Lyapunov theory, Barbalat's lemma, adaptive control theory and algebraic graph theory. Finally, a simulation example is given to validate our theoretical results.

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Moroz, M. "Optimization of Соссinellidae trophism in conditions of biodynamic farming." Interdepartmental Thematic Scientific Collection of Plant Protection and Quarantine, no. 64 (November 19, 2018): 106–12. http://dx.doi.org/10.36495/1606-9773.2018.64.106-112.

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The results of research on the influence of artificial diet on the ontogeny of predatory Соссinellidae are presented. According to the results of the studies, in the experimental variants, the maximum rates of enozytoid hemocyte hemolymph, viability and fertility of the female predatory Соссinellidae were observed. It has been established that an optimized artificial diet provides adaptive plasticity of entomophages in the ontogenesis period, and can be used for the reproduction of Соссinellidae as biological agents for limiting the harmfulness of phytophages in biodynamic agriculture.

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Watson, Richard A., Rob Mills, and C. L. Buckley. "Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale." Artificial Life 17, no. 3 (July 2011): 147–66. http://dx.doi.org/10.1162/artl_a_00029.

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In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.

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Di Caro, G., and M. Dorigo. "AntNet: Distributed Stigmergetic Control for Communications Networks." Journal of Artificial Intelligence Research 9 (December 1, 1998): 317–65. http://dx.doi.org/10.1613/jair.530.

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This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

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Guleva, Valentina, Egor Shikov, Klavdiya Bochenina, Sergey Kovalchuk, Alexander Alodjants, and Alexander Boukhanovsky. "Emerging Complexity in Distributed Intelligent Systems." Entropy 22, no. 12 (December 19, 2020): 1437. http://dx.doi.org/10.3390/e22121437.

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Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

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Strömbom, Daniel, Richard P. Mann, Alan M. Wilson, Stephen Hailes, A. Jennifer Morton, David J. T. Sumpter, and Andrew J. King. "Solving the shepherding problem: heuristics for herding autonomous, interacting agents." Journal of The Royal Society Interface 11, no. 100 (November 6, 2014): 20140719. http://dx.doi.org/10.1098/rsif.2014.0719.

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Herding of sheep by dogs is a powerful example of one individual causing many unwilling individuals to move in the same direction. Similar phenomena are central to crowd control, cleaning the environment and other engineering problems. Despite single dogs solving this ‘shepherding problem’ every day, it remains unknown which algorithm they employ or whether a general algorithm exists for shepherding. Here, we demonstrate such an algorithm, based on adaptive switching between collecting the agents when they are too dispersed and driving them once they are aggregated. Our algorithm reproduces key features of empirical data collected from sheep–dog interactions and suggests new ways in which robots can be designed to influence movements of living and artificial agents.

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Niazi, Muaz A. "Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/140309.

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The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems.

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Manoonpong, Poramate, Xiaofeng Xiong, and Jørgen Christian Larsen. "Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference)." Adaptive Behavior 28, no. 3 (November 27, 2019): 125–27. http://dx.doi.org/10.1177/1059712319888814.

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The Special Issue contains the selected articles presented at the 15th International Conference on the Simulation of Adaptive Behavior (SAB 2018). The conference took place during August 2018 in Frankfurt, Germany. The articles introduce different aspects of closed-loop dynamic computations for adaptive behavior in artificial agents. The aspects cover a range of adaptive behavior research from morphological computation to brain-body-environment interactions, nature-inspired special perception, and closed-loop online learning. SAB is a biennial conference; its next incarnation will be during September 2020, in Paris. If the papers in this issue inspire you, please consider submitting your work to the 2020 conference—who knows, next time it may be your paper in the Special Issue.

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Griol, David, Jesús García-Herrero, and José Manuel Molina. "Combining heterogeneous inputs for the development of adaptive and multimodal interaction systems." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2, no. 3 (November 25, 2013): 37–53. http://dx.doi.org/10.14201/adcaij2014263753.

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In this paper we present a novel framework for the integration of visual sensor networks and speech-based interfaces. Our proposal follows the standard reference architecture in fusion systems (JDL), and combines different techniques related to Artificial Intelligence, Natural Language Processing and User Modeling to provide an enhanced interaction with their users. Firstly, the framework integrates a Cooperative Surveillance Multi-Agent System (CS-MAS), which includes several types of autonomous agents working in a coalition to track and make inferences on the positions of the targets. Secondly, enhanced conversational agents facilitate human-computer interaction by means of speech interaction. Thirdly, a statistical methodology allows modeling the user conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. A technique is proposed to facilitate the multimodal fusion of these information sources and consider the result for the decision of the next system action.

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Ji, Shu-juan, Ho-fung Leung, Kwang Mong Sim, Yong-quan Liang, and Dickson K. W. Chiu. "An adaptive prediction-regret driven strategy for one-shot bilateral bargaining software agents." Expert Systems with Applications 42, no. 1 (January 2015): 411–25. http://dx.doi.org/10.1016/j.eswa.2014.07.022.

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Zheng, Wen, Xia Jin, and Bo Zhang. "The Simulation Study Based on Multi-Agents of Mobile Market." Advanced Materials Research 143-144 (October 2010): 433–38. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.433.

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The holistic simulation study is based on multi-agents to stimulate the behavior of mobile communication. The paper intents to understand the effect of the regulation on the market of mobile communication on the methodology of complex adaptive system simulation in swarm platform. The simulation was on the condition of mimic surrounding. The simulation program includes four classes of Swarm objectives. Results reveal that reasonable, ordered and co-coordinated communication market is on the state of the regulatory of duopoly. Based on the multiple agents modeling on the platform of swarm software, we engendered an artificial mimic mobile market to imitate the mobile telecommunication market. The effect of regulatory rules can be adjusted as the time going by. The best effect appeared at the middle stage of the policy in practice.

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Preux, Philippe, Samuel Delepoulle, and Jean-Claude Darcheville. "A generic architecture for adaptive agents based on reinforcement learning." Information Sciences 161, no. 1-2 (April 2004): 37–55. http://dx.doi.org/10.1016/j.ins.2003.03.005.

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Li, Yongming, Xiaoping Zeng, Liang Han, and Pin Wang. "Two coding based adaptive parallel co-genetic algorithm with double agents structure." Engineering Applications of Artificial Intelligence 23, no. 4 (June 2010): 526–42. http://dx.doi.org/10.1016/j.engappai.2009.04.004.

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Pinto, Tiago, Zita Vale, Tiago M. Sousa, Isabel Praça, Gabriel Santos, and Hugo Morais. "Adaptive learning in agents behaviour: A framework for electricity markets simulation." Integrated Computer-Aided Engineering 21, no. 4 (May 29, 2014): 399–415. http://dx.doi.org/10.3233/ica-140477.

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Azevedo, Roger, Gautam Biswas, Dan Bohus, Ted Carmichael, Mark Finlayson, Mirsad Hadzikadic, Catherine Havasi, et al. "Reports of the AAAI 2010 Fall Symposia." AI Magazine 32, no. 1 (March 16, 2011): 93. http://dx.doi.org/10.1609/aimag.v32i1.2338.

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The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents ; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

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Blisard, Sam, Ted Carmichael, Li Ding, Tim Finin, Wende Frost, Arthur Graesser, Mirsad Hadzikadic, et al. "Reports of the AAAI 2011 Fall Symposia." AI Magazine 33, no. 1 (March 15, 2012): 71–78. http://dx.doi.org/10.1609/aimag.v33i1.2391.

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The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.

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Journal articles on the topic 'Artificial adaptive agents'

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