A Multiagent Semantics for the Game Description Language Stephan Schiffel and Michael Thielscher Technical University of Dresden Dresden, Germany {stephan.schiffel,mit}@inf.tu-dresden.de Abstract. The Game Description Language (GDL) has been developed for the purpose of formalizing game rules. It serves as the input language for general game players, which are systems that learn to play previously unknown games without human intervention. In this paper, we show how GDL descriptions can be intepreted as multiagent domains and, conversely, how a large class of mul- tiagent environments can be speciﬁed in GDL. The resulting speciﬁcations are declarative, compact, and easy to understand and maintain. At the same time they can be fully automatically understood and used by autonomous agents who intend to participate in these environments. Our main result is a formal characterization of the class of multiagent domains that serve as formal semantics for—and can be described in—the Game Description Language. 1 Introduction A novel and challenging research problem for Artiﬁcial Intelligence, General Game Playing is concerned with the development of systems that learn to play a previously unknown game solely on the basis of the rules. The Game Description Language (GDL) [1] has been developed to formalize the rules of any ﬁnite, information-symmetric n- player game in such a way that the description can be automatically processed by a general game player [2]. As a declarative language, GDL supports speciﬁcations that are modular and easy to develop, understand, and maintain. While the basic semantics for GDL is grounded in standard logic, the language uses several pre-deﬁned predicates as keywords, whose intended meaning is only informally described in [1]. In this paper, we show that GDL can be understood as a speciﬁcation language for a large class of multiagent environments. This allows for formalizing the physics and laws that govern an arbitrary domain in such a way that agents can automatically under- stand the rules and thus know how to participate in this environment. There is a variety of potential applications for machine processable descriptions of multiagent environ- ments: the rules of an e-marketplace can be made accessible to agents, the interface of interactive Internet platforms for software agents can be formally described, and agent competitions can be run without revealing detailed problem speciﬁcations in advance. In each of these cases, an autonomous agent—or a team of agents—can learn how to participate in a new or modiﬁed environment without the need to be (re-)programmed for each speciﬁc case. Because GDL uses a decidable subset of logic programming, J. Filipe, A. Fred, and B. Sharp (Eds.): ICAART 2009, CCIS 67, pp. 44–55, 2010. c  Springer-Verlag Berlin Heidelberg 2010