Overview of 97 For the RoboCup Synthetic Agent Challenge 97, we offer three specific targets, critical not only for RoboCup but also for general AI research. These challenges will specifically deal with the software agent league, rather than the real robot league. (Challenges for physical robots will be described elsewhere.) The fundamental issue for researchers who wish to build a team for RoboCup is to design a multiagent system that behaves in real-time, performing reasonable goal-directed behaviors. Goals and situations change dynamically and in real-time. Because the state-space of the soccer game is prohibitively large for anyone to hand-code all possible situations and agent behaviors, it is essential that agents learn to play the game strategically. Research issues on this aspect of the challenge involves: (1) machine learning in a multiagent, collaborative and adversarial environment, (2) multiagent architectures, enabling real-time multiagent planning and plan execution in service of teamwork, and (3) opponent modelling. Therefore, we propose following three challenges as areas of concentration for the RoboCup Synthetic Agent Challenge 97: Learning challenge Teamwork challenge Opponent modeling challenge Evaluating how well competing teams meet these challenges in RoboCup is clearly difficult. If the task is to provide the fastest optimization algorithm for a certain problem, or to prove a certain theorem, the criteria are evident. However, in RoboCup, while there may be a simple test set to examine basic skills, it is not generally possible to evaluate the goodness of a team until they actually play a game. Furthermore, a standard, highly skilled team of opponents is useful to set an absolute basis for such evaluation. We hope to use hand-coded teams, possibly with highly domain-specific coordination, to provide such a team of opponents. Indeed, in a series of preliminary competitions such as PreRoboCup-96 held at the IROS-96 conference, and several other local competitions, teams with well-designed hand-coded behaviors, but without learning and planning capabilities, have performed better than teams with learning and planning schemes. Of course, these hand-coded teams enjoyed the advantage of very low game complexities in initial stages of RoboCup --- increasingly complex team behaviors, tactics and strategies will necessitate agents to face up to the challenges of learning, teamwork and opponent modeling. Therefore, responses to this challenge will be evaluated based on (1) their performance against some standard hand-coded teams as well as other teams submitted as part of the competition; (2) behaviors where task specific constraints are imposed, such as probabilistic occurance of unexpected events, (3) a set of task specific sequences, and (4) novelty and technical soundess of the apporach.
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