Proceedings of the 2005 IJCAI Workshop on Reasoning
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Proceedings of the 2005 IJCAI Workshop on Reasoning, Representation, and Learning in Computer Games (http://home.earthlink.net/~dwaha/research/meetings/ijcai05-rrlcgw) David W. Aha, Héctor Muñoz-Avila, & Michael van Lent (Eds.) Edinburgh, Scotland 31 July 2005 Workshop Committee David W. Aha, Naval Research Laboratory (USA) Daniel Borrajo, Universidad Carlos III de Madrid (Spain) Michael Buro, University of Alberta (Canada) Pádraig Cunningham, Trinity College Dublin (Ireland) Dan Fu, Stottler-Henke Associates, Inc. (USA) Joahnnes Fürnkranz, TU Darmstadt (Germany) Joseph Giampapa, Carnegie Mellon University (USA) Héctor Muñoz-Avila, Lehigh University (USA) Alexander Nareyek, AI Center (Germany) Jeff Orkin, Monolith Productions (USA) Marc Ponsen, Lehigh University (USA) Pieter Spronck, Universiteit Maastricht (Netherlands) Michael van Lent, University of Southern California (USA) Ian Watson, University of Auckland (New Zealand) Aha, D.W., Muñoz-Avila, H., & van Lent, M. (Eds.) (2005). Reasoning, Representation, and Learning in Computer Games: Proceedings of the IJCAI Workshop (Technical Report AIC-05). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence. Preface These proceedings contain the papers presented at the Workshop on Reasoning, Representation, and Learning in Computer Games held at the 2005 International Joint Conference on Artificial Intelligence (IJCAI’05) in Edinburgh, Scotland on 31 July 2005. Our objective for holding this workshop was to encourage the study, development, integration, and evaluation of AI techniques on tasks from complex games. These challenging performance tasks are characterized by huge search spaces, uncertainty, opportunities for coordination/teaming, and (frequently) multi-agent adversarial conditions. We wanted to foster a dialogue among researchers in a variety of AI disciplines who seek to develop and test their theories on comprehensive intelligent agents that can function competently in virtual gaming worlds. We expected that this workshop would yield an understanding of (1) state-of-the-art approaches for performing well in complex gaming environments and (2) research issues that require additional attention. Games-related research extends back to the origins of artificial intelligence, which includes Turing’s proposed imitation game and Arthur Samuel’s work on checkers. Several notable achievements have been attained for the games of checkers, reversi, Scrabble, backgammon, and chess, among several others. Several AI journals are devoted to this topic, such as the International Computer Games Association Journal, the International Journal of Intelligent Games and Simulation, and the Journal of Game Development. Similarly, many conferences are likewise devoted to this topic, including the International Conference on Computers and Games, the European Conference on Simulation and AI in Computer Games, and the new Conference on Artificial Intelligence and Interactive Digital Entertainment. Naturally, IJCAI has also hosted workshops on AI research and games, including Entertainment and AI/Alife (IJCAI’95), Using Games as an Experimental Testbed for AI Research (IJCAI’97), and RoboCup (IJCAI’97, IJCAI’99). In contrast to previous IJCAI workshops on AI and games, this one has a relatively broad scope; it was not focused on a specific sub-topic (e.g., testbeds), game or game genre, or AI reasoning paradigm. Rather, we focused on topics of general interest to AI researchers (i.e., reasoning, representation, learning) to which many different types of AI approaches could apply. Thus, this workshop provided an opportunity to share and learn from a wide variety of research perspectives, which is not feasible for meetings held at conferences on AI sub-disciplines. Therefore, our agenda was quite broad. Our invited speakers included Ian Davis, who discussed applied research at Mad Doc Software, and Michael Genesereth/Nat Love, who reported on the first annual General Game Playing Competition (at AAAI’05) and their future plans. In addition to sessions of presentations and discussion periods on the workshop’s three themes (i.e., reasoning, representation, and learning), our fourth session focused on AI architectures. We also held a sizable poster session (i.e., perhaps we should have scheduled this as a 2-day event) and a wrap-up panel that generated visions for future research and development, including feasible and productive suggestions for dissertation topics. The Workshop Committee did a great job in providing suggestions and informative reviews for the submissions; thank you! Thanks also to Carlos Guestrin, ICCBR’05 Workshop Chair, for his assistance in helping us to hold and schedule this workshop. Finally, thanks to all the participants; we hope you found this to be useful! David W. Aha, Héctor Muñoz-Avila, & Michael van Lent Edinburgh, Scotland 31 July 2005 ii Table of Contents Title Page i Preface ii Table of Contents iii Hazard: A Framework Towards Connecting Artificial Intelligence and Robotics 1 Peter J. Andersson Extending Reinforcement Learning to Provide Dynamic Game Balancing 7 Gustavo Andrade, Geber Ramalho, Hugo Santana, & Vincent Corruble Best-Response Learning of Team Behaviour in Quake III 13 Sander Bakkes, Pieter Spronck, & Eric Postma OASIS: An Open AI Standard Interface Specification to Support Reasoning, Representation and Learning in Computer Games 19 Clemens N. Berndt, Ian Watson, & Hans Guesgen Colored Trails: A Formalism for Investigating Decision-Making in Strategic Environments 25 Ya’akov Gal, Barbara J. Grosz, Sarit Kraus, Avi Pfeffer, & Stuart Shieber Unreal GOLOG Bots 31 Stefan Jacobs, Alexander Ferrein, & Gerhard Lakemeyer Knowledge Organization and Structural Credit Assignment 37 Joshua Jones & Ashok Goel Requirements for Resource Management Game AI 43 Steven de Jong, Pieter Spronck, & Nico Roos Path Planning in Triangulations 49 Marcelo Kallmann Interfacing the D’Artagnan Cognitive Architecture to the Urban Terror First-Person Shooter Game 55 Bharat Kondeti, Maheswar Nallacharu, Michael Youngblood, & Lawrence Holder Knowledge-Based Support-Vector Regression for Reinforcement Learning 61 Rich Maclin, Jude Shavlik, Trevor Walker, & Lisa Torrey Writing Stratagus-playing Agents in Concurrent ALisp 67 Bhaskara Marthi, Stuart Russell, & David Latham Defeating Novel Opponents in a Real-Time Strategy Game 72 Matthew Molineaux, David W. Aha, & Marc Ponsen Stratagus: An Open-Source Game Engine for Research in Real-Time Strategy Games 78 Marc J.V. Ponsen, Stephen Lee-Urban, Héctor Muñoz-Avila, David W. Aha, & Matthew Molineaux Towards Integrating AI Story Controllers and Game Engines: Reconciling World State Representations 84 Mark O. Riedl An Intelligent Decision Module based on CBR for C-evo 90 Rubén Sánchez-Pelegrín & Belén Díaz-Agudo iii A Model for Reliable Adaptive Game Intelligence 95 Pieter Spronck Knowledge-Intensive Similarity-based Opponent Modeling 101 Timo Steffens Using Model-Based Reflection to Guide Reinforcement Learning 107 Patrick Ulam, Ashok Goel, Joshua Jones, & William Murdock The Design Space of Control Options for AIs in Computer Games 113 Robert E. Wray, Michael van Lent, Jonathan Beard, & Paul Brobst A Scheme for Creating Digital Entertainment with Substance 119 Georgios N. Yannakakis & John Hallam Author Index 125 iv Hazard: A Framework Towards Connecting Artificial Intelligence and Robotics Peter J. Andersson Department of Computer and Information Science, Linköping university [email protected] Abstract courages focus on the high-level control of the robot, but since there are no wrappers to high-level AI frameworks, it The gaming industry has started to look for so- does not encourage reuse of existing AI techniques. By de- lutions in the Artificial intelligence (AI) research veloping a high-level interface between Player-Stage and AI community and work has begun with common frameworks, we will also allow AI researchers to take advan- standards for integration. At the same time, few tage of the Player-Stage project. robotic systems in development use already de- veloped AI frameworks and technologies. In this The Robocup initiative [Kitano et al., 1997] uses both ac- article, we present the development and evalua- tual robotic hardware and simulation in competition. Yet, tion of the Hazard framework that has been used there exists no common interface for using simulation league to rapidly create simulations for development of AIs with robotic league robots. This can mean that the sim- cognitive systems. Implementations include for ulation interface is unintuitive for actual robotics, or that AIs example a dialogue system that transparently can developed with the simulation are not usable with actual ro- connect to either an Unmanned Aerial Vehicle bots. In either case it is a problem worth investigating. (UAV) or a simulated counterpart. Hazard is [ found suitable for developing simulations support- The WITAS Unmanned Aerial Vehicle project Doherty ] ing high-level AI development and we identify and et al., 2000 uses several simulators in their research, both propose a solution to the factors that make the for hardware-in-the-loop simulation of the helicopter hard- framework unsuitable for lower level robotic spe- ware and for development of dialogue interaction with an ac- cific tasks such as event/chronicle recognition. tual UAV. A middleware translating actions and events from WITAS protocol to other protocols would allow experimen- tation with for example SOAR [Laird et al., 1987] as a