Neural Networks for State Evaluation in General Game Playing

Abstract. Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game’s real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.

Imaging of adult leukodystrophies

Leukodystrophies are genetically determined white matter disorders. Even though leukodystrophies essentially affect children in early infancy and childhood, these disorders may affect adults. In adults, leukodystrophies may present a distinct clinical and imaging presentation other than those found in childhood. Clinical awareness of late-onset leukodystrophies should be increased as new therapies emerge. MRI is a useful tool to evaluate white matter disorders and some characteristics findings can help the diagnosis of leukodystrophies. This review article briefly describes the imaging characteristics of the most common adult leukodystrophies

A Multiagent Semantics for the Game Description Language

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 interpreted as multiagent domains and, conversely, how a large class of multiagent 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

Answer Set Programming for Single-Player Games in General Game Playing

Abstract. As a novel, grand AI challenge, General Game Playing is concerned with the development of systems that understand the rules of unknown games and play these games well without human intervention. In this paper, we show how Answer Set Programming can assist a general game player with the special class of single-player games. To this end, we present a translation from the general Game Description Language (GDL) into answer set programs (ASP). Correctness of this mapping is established by proving that the stable models of the resulting ASP coincide with the possible developments of the original GDL game. We report on experiments with single-player games from past AAAI General Game Playing Competitions which substantiate the claim that Answer Set Programming can provide valuable support to general game playing systems for this type of games.

Modular Simulation of Knowledge Development in Industry: A Multi-Level Framework

Abstract. Innovation is a central element of economic development. Understanding knowledge – its organization and especially its dynamics in a market – becomes therefore the main challenge when explaining economic development in general, and the competitiveness and growth of firms and industries in particular. Past research has generally treated knowledge as a monolithic object rather than a composite dynamic phenomenon. In this paper we present work on a new fine-grain, dynamic, morphogenic model of knowledge that is easy to manage, interpret and extend. This knowledge model is embedded a larger market simulation where selected elements of an economy, including employees, companies, banks and consumers, are modeled at multiple levels of abstraction, from agents to monolithic entities. We present data from early runs of the system, showing predictable results in baseline conditions and product innovation effects using the knowledge representation. The results show the model’s excellent potential to address questions about emergent phenomena related to knowledge evolution, knowledge transfer and knowledge use in market innovation