Artificial Intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA-CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.

[1]  Peggy A. Ertmer,et al.  Behaviorism, Cognitivism, Constructivism: Comparing Critical Features From an Instructional Design Perspective , 2008 .

[2]  Araceli Sanchis,et al.  A Machine Consciousness Approach to the Design of Human-Like Bots , 2012, Believable Bots.

[3]  Peggy A. Ertmer,et al.  Behaviorism, Cognitivism, Constructivism: Comparing Critical Features from a Design Perspective. , 1993 .

[4]  Michael L. Anderson.  Embodied Cognition: A field guide , 2003, Artif. Intell..

[5]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[6]  Csaba Szepesvári,et al.  Algorithms for Reinforcement Learning , 2010, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[7]  Giulio Sandini,et al.  A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents , 2007, IEEE Transactions on Evolutionary Computation.

[8]  L. Darrell Whitley,et al.  Optimizing Neural Networks Using FasterMore Accurate Genetic Search , 1989, ICGA.

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

[10]  Julian Togelius,et al.  Experience-Driven Procedural Content Generation , 2011, IEEE Trans. Affect. Comput..

[11]  Alexei V. Samsonovich,et al.  Toward a Unified Catalog of Implemented Cognitive Architectures , 2010, BICA.

[12]  Risto Miikkulainen,et al.  Human-Like Combat Behaviour via Multiobjective Neuroevolution , 2012, Believable Bots.

[13]  Julian Togelius,et al.  Assessing Believability , 2012, Believable Bots.

[14]  Alexander Nareyek,et al.  AI in Computer Games , 2004, ACM Queue.

[15]  Angel Domingo Sappa,et al.  A general-purpose crowdsourcing platform for mobile devices , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[16]  Alicia Fornés,et al.  Divide and conquer: atomizing and parallelizing a task in a mobile crowdsourcing platform , 2013, CrowdMM '13.

[17]  Christine L. Lisetti,et al.  Emotions and personality in agent design , 2002, AAMAS '02.

[18]  H. Penny Nii.  Blackboard systems at the architecture level , 1994 .

[19]  Philip Hingston,et al.  A new design for a Turing Test for Bots , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[20]  Philip Hingston,et al.  A Turing Test for Computer Game Bots , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[21]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[22]  José Neves,et al.  Evolution of neural networks for classification and regression , 2007, Neurocomputing.

[23]  J. Singer,et al.  Cognitive, social, and physiological determinants of emotional state. , 1962, Psychological review.

[24]  Risto Miikkulainen,et al.  UT2: Human-like behavior via neuroevolution of combat behavior and replay of human traces , 2011, CIG.

[25]  B. Baars.  IN THE THEATRE OF CONSCIOUSNESS Global Workspace Theory, A Rigorous Scientific Theory of Consciousness. , 1997 .

[26]  Rudolf Kadlec,et al.  Pogamut 3 Can Assist Developers in Building AI (Not Only) for Their Videogame Agents , 2009, AGS.

[27]  Daniel Dennett.  Caveat Emptor (reply to essays on Consciousness Explained - reply to Mangan, Toribio, Baars and McGovern) in , 1993 .

[28]  Araceli Sanchis,et al.  ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots , 2012, Believable Bots.

[29]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.