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 mind.

[1]  Gordon S. Blair,et al.  Models@ run.time , 2009, Computer.

[2]  Edwina L. Rissland,et al.  Cognitive Science: An Introduction , 1987 .

[3]  John R. Anderson,et al.  ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual Attention , 1997, Hum. Comput. Interact..

[4]  Olivier Boissier,et al.  Multi-agent oriented programming with JaCaMo , 2013, Sci. Comput. Program..

[5]  Michael Wooldridge,et al.  Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology) , 2007 .

[6]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[7]  M. Georgeff,et al.  Rational software agents: from theory to practice , 1998 .

[8]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[9]  A. Opstal Dynamic Patterns: The Self-Organization of Brain and Behavior , 1995 .

[10]  Mary Shaw,et al.  Software Engineering for Self-Adaptive Systems: A Research Roadmap , 2009, Software Engineering for Self-Adaptive Systems.

[11]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[12]  Valeria Seidita,et al.  Knowledge acquisition through introspection in Human-Robot Cooperation , 2018, BICA 2018.

[13]  Valeria Seidita,et al.  A Cognitive Architecture for Human-Robot Teaming Interaction , 2018, AIC.

[14]  A. Clark Mindware: An Introduction to the Philosophy of Cognitive Science (Second Edition) , 2000 .

[15]  Ron Sun,et al.  The importance of cognitive architectures: an analysis based on CLARION , 2007, J. Exp. Theor. Artif. Intell..

[16]  Valeria Seidita,et al.  Representing and Developing Knowledge using Jason, Cartago and OWL , 2018, WOA.

[17]  Tamas Madl,et al.  LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning , 2014, IEEE Transactions on Autonomous Mental Development.

[18]  David E. Kieras,et al.  An Overview of the EPIC Architecture for Cognition and Performance With Application to Human-Computer Interaction , 1997, Hum. Comput. Interact..

[19]  Rino Falcone,et al.  Trust Theory: A Socio-Cognitive and Computational Model , 2010 .

[20]  Jesper Andersson,et al.  Software Engineering Processes for Self-Adaptive Systems , 2013, Software Engineering for Self-Adaptive Systems.

[21]  Valeria Seidita,et al.  FROM MODELING TO IMPLEMENTING THE PERCEPTION LOOP IN SELF-CONSCIOUS SYSTEMS , 2010 .

[22]  Anand S. Rao,et al.  AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language , 1996, MAAMAW.

[23]  Luciano Baresi,et al.  The disappearing boundary between development-time and run-time , 2010, FoSER '10.

[24]  John E. Laird,et al.  A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics , 2017, AI Mag..

[25]  M. Shanahan,et al.  Applying global workspace theory to the frame problem , 2005, Cognition.

[26]  Cliff Hooker,et al.  Representation and the Meaning of Life , 2004 .

[27]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..