A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

FAST : a fault detection and identification software tool

The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities. Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix. To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent. FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilització d'eines per la detecció i identificació de fallades (FDI) en la indústria, es requereix un enfocament sistemàtic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundància d'acord amb la seva criticitat. A més, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procés (per exemple afegint sensors o canviant la seva localització) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundància. Per tant, aquest projecte proposa un enfocament per analitzar un procés des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anàlisi i disseny fins a la implementació final d'un supervisor FDI en un procés real. Per sintetitzar la informació del procés s'ha definit un format simple basat en XML. Aquest format proporciona la informació necessària per realitzar de forma sistemàtica l'Anàlisi Estructural del procés. Qualsevol procés pot ser analitzat, només hi ha la restricció de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procés, components i relacions i l'eina realitza l'anàlisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundància analítica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procés d'anàlisi, FAST pot operar aïlladament en mode de simulació permetent a l'enginyer de procés avaluar fallades, la seva detectabilitat i implementar canvis en els components del procés i la topologia per tal de millorar les capacitats de diagnosi i redundància. Per altra banda, FAST pot operar en línia connectat al procés de la planta per mitjà d'una interfície OPC. La interfície OPC permet la possibilitat de connectar gairebé a qualsevol procés que inclogui un sistema SCADA per la seva supervisió. Quan funciona en mode en línia, el procés està monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST té la capacitat d'implementar FDI de forma distribuïda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procés han de ser compartides per cada subsistema i generar una instància d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procés FDI de forma distribuïda.Postprint (published version

Automated Federation Of Virtual Organization In Grid Using Select, Match, Negotiate And Expand (SMNE) Protocol [QA76.9.C58 C518 2008 f rb].

Sekelompok sumber perkomputeran yang teragih dan berlainan jenis dalam persekitaran grid akan membentuk organisasi maya dan berkongsi sumber komputer. A group of distributed and heterogeneous resources in a grid environment may form a Virtual Organization (VO) to enable resource sharing

Towards Formal Modeling of Affective Agents in a BDI Architecture

[EN] Affective characteristics are crucial factors that influence human behavior, and often the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture (GenIA3). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, and the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent s affective and rational sides.This work was supported by the Generalitat Valenciana grant PROMETEOII/2013/019, and the Spanish TIN2014-55206-R project of the Ministerio de Economa y Competitividad.Alfonso Espinosa, B.; Vivancos, E.; Botti, V. (2017). Towards Formal Modeling of Affective Agents in a BDI Architecture. ACM Transactions on Internet Technology. 17(1):5:1-5:23. https://doi.org/10.1145/3001584S5:15:23171Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2014. An open architecture for affective traits in a BDI agent. In Proceedings of the 6th ECTA 2014. Part of the 6th IJCCI 2014. 320--325.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016a. Design of an Affective Intelligent Agent on GenIA. Technical Report. DSIC, UPV, Spain.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016b. Toward a Systematic Development of Affective Intelligent Agents. Technical Report. DSIC, UPV, Spain.Gordon Willard Allport. 1937. Personality: A Psychological Interpretation. Henry Holt, New York.Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191.Cristina Battaglino, Rossana Damiano, and Leonardo Lesmo. Emotional range in value-sensitive deliberation. In Proceedings of AAMAS’13. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 769--776.Antoine Bechara, Hanna Damasio, and Antonio R Damasio. 2000. Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex 10, 3 (2000), 295--307.Rafael H. Bordini and Jomi Fred Hübner. 2010. Semantics for the Jason variant of AgentSpeak (plan failure and some internal actions). In Proceedings of ECAI’10. IOS Press, Amsterdam, The Netherlands, 635--640.Rafael H. Bordini, Jomi Fred Hübner, and Michael Wooldridge. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley.Tibor Bosse, Joost Broekens, João Dias, and Janneke van der Zwaan. 2014. Emotion Modeling. Springer.Scott Brave, Clifford Nass, and Kevin Hutchinson. 2005. Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent. International Journal of Human-computer Studies 62, 2 (2005), 161--178.Jerome R. Busemeyer, Eric Dimperio, and Ryan K. Jessup. 2007. Integrating Emotional Processes Into Decision-Making Models. Oxford University Press, 29--44.Colin F Camerer, George Loewenstein, and Matthew Rabin. 2011. Advances in Behavioral Economics. Princeton University Press.Martin A Conway. 1990. Autobiographical Memory: An Introduction. Open University Press.Ronald De Sousa. 1990. The Rationality of Emotion. MIT Press.João Dias, Samuel Mascarenhas, and Ana Paiva. 2014. FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework. Springer International Publishing, 44--56. DOI:http://dx.doi.org/10.1007/978-3-319-12973-0_3Magy Seif El-Nasr, John Yen, and Thomas R Ioerger. 2000. Flame—fuzzy logic adaptive model of emotions. Autonomous Agents and Multi-agent systems 3, 3 (2000), 219--257.Hans Jürgen Eysenck. 1982. Personality, Genetics, and Behavior: Selected Papers. Praeger, Chapter Development of a Theory, 1--48.Shane Frederick. 2005. Cognitive reflection and decision making. The Journal of Economic Perspectives 19, 4 (2005), 25--42.N. H. Frijda, A. S. R. Manstead, and S. Bem. 2000. Emotions and Beliefs: How Feelings Influence Thoughts. Cambridge University Press.Nico H. Frijda. 2007. The Laws of Emotion. Lawrence Erlbaum Associates, Incorporated.Patrick Gebhard. 2005. ALMA: A layered model of affect. In Proceedings of the 4th AAMAS. ACM, New York, NY, 29--36. DOI:http://dx.doi.org/10.1145/1082473.1082478Lewis R. Goldberg and others. 1990. An alternative “description of personality”: The big-five factor structure. Journal of Personality and Social Psychology 59, 6 (1990), 1216--1229.James J. Gross and Ross A. Thompson. 2011. Emotion regulation: Conceptual fundations. In Handbook of Emotion Regulation. Guilford Publications.JonathanY. Ito, DavidV. Pynadath, and StacyC. Marsella. 2010. Modeling self-deception within a decision-theoretic framework. AAMAS 20, 1 (2010), 3--13. DOI:http://dx.doi.org/10.1007/s10458-009-9096-7William G. Kennedy. 2012. Modelling human behaviour in agent-based models. In Agent-based Models of Geographical Systems. Springer, 167--179.Jonathan Klein, Youngme Moon, and Rosalind W. Picard. 2002. This computer responds to user frustration: Theory, design, and results. Interacting with Computers 14, 2 (2002), 119--140.Richard S. Lazarus and Susan Folkman. 1984. Stress, Appraisal, and Coping. Springer.Stacy Marsella and Jonathan Gratch. 2003. Modeling coping behavior in virtual humans: Don’t worry, be happy. In Proceedings of AAMAS’03. ACM, 313--320. DOI:http://dx.doi.org/10.1145/860575.860626Stacy C. Marsella and Jonathan Gratch. 2009. EMA: A process model of appraisal dynamics. Cognitive Systems Research 10, 1 (2009), 70--90.Stacy C. Marsella, Jonathan Gratch, and Paolo Petta. 2010. Computational models of emotion. In A Blueprint for Affective Computing: A Sourcebook and Manual. OUP Oxford, 21--46.Robert R. McCrae and Oliver P. John. 1992. An introduction to the five-factor model and its applications. Journal of Personality 60, 2 (1992), 175--215.Albert Mehrabian. 1996a. Analysis of the big-five personality factors in terms of the PAD temperament model. Australian Journal of Psychology 48, 2 (1996), 86--92. DOI:http://dx.doi.org/10.1080/00049539608259510Albert Mehrabian. 1996b. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14, 4 (1996), 261--292. DOI:http://dx.doi.org/10.1007/BF02686918Albert Mehrabian and James A. Russell. 1974. An Approach to Environmental Psychology. MIT Press.John-Jules Ch. Meyer. 2006. Reasoning about emotional agents. International Journal of Intelligent Systems 21, 6 (June 2006), 601--619. DOI:http://dx.doi.org/10.1002/int.v21:6Katherine Nelson. 1993. The psychological and social origins of autobiographical memory. Psychological Science 4, 1 (1993), 7--14.Magalie Ochs, David Sadek, and Catherine Pelachaud. 2012. A formal model of emotions for an empathic rational dialog agent. AAMAS 24, 3 (2012), 410--440. DOI:http://dx.doi.org/10.1007/s10458-010-9156-zAndrew Ortony. 2003. On making believable emotional agents believable. In Emotions in Humans and Artifacts, R. P. Trapple, P. Petta, and S. Payer (Eds.). MIT Press, Chapter 6, 189--212.Andrew Ortony, Gerald L. Clore, and Allan Collins. 1988. The Cognitive Structure of Emotions. Cambridge University Press.Rosalind W. Picard and Karen K. Liu. 2007. Relative subjective count and assessment of interruptive technologies applied to mobile monitoring of stress. International Journal of Human-Computer Studies 65, 4 (2007), 361--375.César F. Pimentel and Maria R. Cravo. 2005. Affective revision. In Progress in Artificial Intelligence, Carlos Bento, Amílcar Cardoso, and Gaël Dias (Eds.). LNCS, Vol. 3808. Springer Berlin, 115--126.Gordon D. Plotkin. 1981. A Structural Approach to Operational Semantics. Technical Report DAIMI FN-19. Aarhus University.Anand S. Rao. 1996. Agentspeak(L): BDI agents speak out in a logical computable language. In Proceedings of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Rudy Van Hoe (Ed.). Eindhoven, The Netherlands.Rainer Reisenzein, Eva Hudlicka, Mehdi Dastani, Jonathan Gratch, Koen Hindriks, Emiliano Lorini, and J-JC Meyer. 2013. Computational modeling of emotion: Toward improving the inter- and intradisciplinary exchange. IEEE Transactions on Affective Computing 4, 3 (2013), 246--266.Luis-Felipe Rodríguez and Félix Ramos. 2014. Development of computational models of emotions for autonomous agents: A review. Cognitive Computation 6, 3 (2014), 351--375. DOI:http://dx.doi.org/10.1007/s12559-013-9244-xIra J. Roseman. 2001. A Model of Appraisal in the Emotion System: Integrating Theory, Research, and Applications. Oxford University Press, 68--91.James A. Russell. 2003. Core affect and the psychological construction of emotion. Psychological Review 110, 1 (2003), 145--172.Klaus R. Scherer. 2001. Appraisal considered as a process of multilevel sequential checking. Appraisal Processes in Emotion: Theory, Methods, Research 92 (2001), 120.Norbert Schwarz. 2000. Emotion, cognition, and decision making. Cognition 8 Emotion 14, 4 (2000), 433--440.Leila Selimbegović, Isabelle Régner, Pascal Huguet, and Armand Chatard. 2015. On the power of autobiographical memories: From threat and challenge appraisals to actual behaviour. Memory (2015), 1--8.Martin Sewell. 2010. Emotions help solve the prisoner’s dilemma. In Proceedings of the Behavioural Finance Working Group Conference: Fairness, Trust and Emotions in Finance, London. 1--2.Craig A. Smith and Richard S. Lazarus. 1990. Emotion and adaptation. In Handbook of Personality: Theory and Research, Lawrence A. Pervin (Ed.). 609--637.Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2009. A formal model of emotion-based action tendency for intelligent agents. In Proceedings of EPIA’09. Springer-Verlag, Berlin, 174--186. DOI:http://dx.doi.org/10.1007/978-3-642-04686-5_15Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2012. A formal model of emotion triggers: An approach for BDI agents. Synthese 185 (2012), 83--129. DOI:http://dx.doi.org/10.1007/s11229-011-0004-8AW Tucker. 1983. The mathematics of tucker: A sampler. The Two-Year College Mathematics Journal 14, 3 (1983), 228--232.Renata Vieira, Álvaro F. Moreira, Michael Wooldridge, and Rafael H. Bordini. 2007. On the formal semantics of speech-act based communication in an agent-oriented programming language. J. Artif. Intell. Res. (JAIR) 29 (2007), 221--267.G. Weiss. 2013. Multiagent Systems. MIT Press

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min

Modeling travel assistant agents: a graded BDI approach

In this paper, we use a graded BDI agent model based on multi-context systems to specify an architecture for a Travel Assistant Agent that helps a tourist to choose holiday packages. We outline the theories of the different contexts and the bridge rules and illustrate the overall reasoning process of our model.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 2Red de Universidades con Carreras en Informática (RedUNCI