First International Workshop on Agents for Games and Simulations Budapest May 11, 2009 i Table of Contents Preface iii Avi Rosenfeld and Sarit Kraus. Modeling Agents through Bounded Rationality Theories 1 Jean-Pierre Briot, Alessandro Sordoni, Eurico Vasconcelos, Gustavo Melo, Marta de Azevedo Irving and Isabelle Alvarez. Design of a Decision Maker Agent for a Distributed Role Playing Game - Experience of the SimParc Project 16 Michael Köster, Peter Novák, David Mainzer and Bernd Fuhrmann. Two Case Studies for Jazzyk BSM 31 Joost Westra, Hado van Hasselt, Frank Dignum and Virginia Dignum. Adaptive serious games using agent organizations 46 D.W.F. van Krevelen. Intelligent Agent Modeling as Serious Game 61 Gustavo Aranda, Vicent Botti and Carlos Carrascosa. The MMOG Layer: MMOG based on MAS 75 Ivan M. Monteiro and Luis O. Alvares. A Teamwork Infrastructure for Computer Games with Real-Time Requirements 90 Barry Silverman, Deepthi Chandrasekaran, Nathan Weyer, David Pietrocola, Robert Might and Ransom Weaver. NonKin Village: A Training Game for Learning Cultural Terrain and Sustainable Counter-Insurgent Operations 106 Mei Yii Lim, Joao Dias, Ruth Aylett and Ana Paiva. Intelligent NPCs for Education Role Play Game 117 Derek J. Sollenberger and Munindar P. Singh. Architecture for Affective Social Games 129 Jakub Gemrot, Rudolf Kadlec, Michal Bída, Ondřej Burkert, Radek Píbil, Jan Havlíček, Juraj Šimlovič, Radim Vansa, Michal Štolba, Lukáš Zemčák and Cyril Brom. Pogamut 3 Can Assist Developers in Building AI for Their Videogame Agents 144 Daniel Castro Silva, Ricardo Silva, Luís Paulo Reis and Eugénio Oliveira. Agent-Based Aircraft Control Strategies in a Simulated Environment 149 ii Preface Multi Agent System research offers a promising technology to implement cognitive intelligent Non Playing Characters. However the technologies used in game engines and multi agent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multi agent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state of the art Multi Agent System technology. A very similar argument can be given for agent based (social) simulations. In this workshop we bring people together that address the particular challenges of using agent technology for games and simulations. Submissions were invited for the following three main themes: 1. Technical: Connecting agent platforms to games and simulation engines; who is in control?, Are actions synchronous or asynchronous? How to monitor results of actions? Can agents communicate through the agent platform? How efficient should the agents be? 2. Conceptual: What information is available for the agents from the game or simulation engine? How to balance reaction to events of the game or simulation with goal directed behavior. Ontological differences between agents and game/simulation information. 3. Design: How to design games/simulations containing intelligent agents. How to design agents that are embedded in other systems Of course we also welcomed papers about experiences on the use of agents in games and simulations. Both successes as well as "failures" were welcome as they both can help us better understand what are the key issues in combining agents with game and simulation engines. We received 17 submissions of high quality covering many of the aspects mentioned above. We accepted 12 papers for presentation, which can be found in this proceedings. Among the papers we find a strong example of a description of middleware that is used to connect agents to Unreal Tournament (an extension of Gamebots). But we also have a paper describing how to design agents for games that have to behave according to bounded rationality theory in order to mimic human behavior. Several papers describe experiences with particular agent models used for games and simulations. All in all we are very happy with the papers contained in this volume. We are sure they form a valuable starting point for people that want to combine agent technology with (serious) games. Finally, we would like to thank the program committee members without whom the reviewing would not have been possible and that gave valuable comments on all papers allowing for a tough selection of the best paper. Frank Dignum, Jeff Bradshaw, Barry Silverman, Willem van Doesburg. iii Organizing Committee Frank Dignum, Utrecht University Jeff Bradshaw, Florida Institute for Human & Machine Cognition Barry Silverman, University of Pennsylvania/Systems Engineering Dept. Willem van Doesburg, TNO Defence, Security and Safety Program Committee • Elisabeth Andre (DFKI, Germany) • Ruth Aylett (Heriot-Watt University, UK) • Andre Campos (UFRN, Brazil) • Bill Clancey (NASA, USA) • Rosaria Conte (ISTC-CNR, Italy) • Vincent Corruble (LIP6, France) • Yves Demazeau (CNRS-LIG, Grenoble) • Virginia Dignum (Utrecht University, The Netherlands) • Alexis Drogoul (LIP6, France) • Bruce Edmonds (MMU, UK) • Corinna Elsenbroich (University of Surrey, UK) • Klaus Fischer (DFKI, Germany) • Hiromitsu Hattori (Kyoto University, Japan) • Koen Hindriks (Delft University, The Netherlands) • Wander Jager (Groningen University, The Netherlands) • Stefan Kopp (University of Bielefeld, Germany) • Michael Lewis (University of Pittsburg, USA) • Scott Moss (MMU, UK) • Emma Norling (MMU, UK) • Anton Nijholt (UT, The Netherlands) • Ana Paiva (IST, Portugal) • Michal Pechoucek (CTU, Czechia) • David Pynadath (USC, USA) • Geber Ramalho (Brazil) • Gopal Ramchurn (University of Southampton, UK) • Avi Rosenfeld (JCT, Israel) • David Sarne (Bar Ilan University, Israel) • Pjotr van Schothorst (VSTEP, The Netherlands) • Maarten Sierhuis (NASA, USA) • Pieter Spronck (Tilburg University, The Netherlands) • Katia Sycara (CMU, USA) • Duane Szafron (U of Alberta, Canada) • Max Tsvetovat (George Mason University, USA) iv Modeling Agents through Bounded Rationality Theories Avi Rosenfeld1 and Sarit Kraus2 1 Jerusalem College of Technology, Jerusalem 91160, Israel [email protected] 2 Bar-Ilan University, Ramat-Gan 52900, Israel [email protected] Abstract. Effectively modeling an agent’s cognitive model is an important prob- lem in many domains. In this paper, we explore the search agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many ele- ments from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive empirical results from over a hundred agents programmed to perform in optimization problems involv- ing solving for one and two variables. 1 Introduction Realistic modeling of individual reasoning and decision making is essential for eco- nomics and artificial intelligence researchers [1, 3–6, 10, 12, 13]. In economics, validly encapsulating human decision-making is critical, for instance in predicting policy ef- fects. Within the field of computer science, it is critical for mixed human-computer sys- tems such as entertainment domains [3], Interactive Tutoring Systems [6], and mixed human-agent trading environments [4]. In these and similar domains, creating agents that effectively understand and/or simulate people’s logic is particularly critical. In both economics and computer science the perspectives of unbounded rationality based on notions such as expected utility, game theory, Bayesian models, or Markov Decision Processes (MDP) [7, 9], have traditionally been the foundation for modeling agent’s behavior. While many important insights have been gained by these perspectives, it often does not provide a descriptively correct model of human decision-making. Indeed, previous research in experimental economics and cognitive psychology has shown that human decision makers often do not adhere to fully rational behavior. For example, Kahne- man and Tversky [2] have shown that individuals often deviate from optimal behavior as prescribed by Expected Utility Theory. Furthermore, decision makers often do not know the quantitative structure of the environment in which they act. But even if the quantitative structure of the environment is known to the decision maker, finding the optimal path of actions is often a problem with intractable computational complexity [8]. Thus, even in the best of circumstances, modeling behavior based on full rationality may be impractical. 1 A research direction called Bounded Rationality initiated by Simon [15] focuses on investigating observed rationality of individuals in decision making and problem solving. Simon presumes that people – except in the most simple situations – lack the cognitive and computational capabilities to find optimal solutions. Instead they proceed by searching for non-optimal alternatives to fulfill their goals. Simon proposes that real- world decision makers satisfice rather than optimize seeking a “good enough” solution instead of the optimal one. In this tradition, Sauermann and Selten propose a framework called Aspiration Adaptation Theory (AAT) [10, 12] as a boundedly rational model of decision making. Recent experimental evidence from economics researchers provides support that people apply