In this paper we describe and analyze models of problem solving and computation going beyond Turing Machines. Three principles of extending the Turing Machine's expressiveness are identified, namely, by interaction, evolution and infinity. Several models utilizing the above principles are presented. Other

“Hypercomputation ” is often defined as transcending Turing computation in the sense of computing a larger class of functions than can Turing machines. While this possibility is important and interesting, this paper argues that there are many other important senses in which we may “transcend Turing computation. ” Turing computation, like all models, exists in a frame of relevance, which underlies the assumptions on which it rests and the questions that it is suited to answer. Although appropriate in many circumstances, there are other important applications of the idea of computation for which this model is not relevant. Therefore we should supplement it with new models based on different assumptions and suited to answering different questions. In alternative frames of relevance, including natural computation and nanocomputation, the central issues include real-time response, continuity, indeterminacy, and parallelism. Once we understand computation in a broader sense, we can see new possibilities for using physical processes to achieve computational goals, which will increase in importance as we approach the limits of electronic binary logic. Key words: hypercomputation, Church-Turing thesis, natural computation, theory of computation, model of computation, Turing computation,

In this paper we present a unified view of AI inspired by ideas from Evolutionary Computation as design of bounded rational agents. The approach specifies optimal programs rather than optimal actions, and is based on process algebras and anytime algorithms. The search method described in this paper is so general than many other search algorithms, including evolutionary search methods, become its special case. In this paper, we present a practical design of the programming language and environment targetting real-time complex domains. As AI systems move into more complex domains, all problems become real-time, because the agent will never have long enough time to solve the decision problem exactly.

In this paper we show the design of a language which is applicable for control of multiple autonomous unmanned vehicles as well as for distributed problem solving, in general. The language, under the working name CCL (Common Control Language) is a natural extension of behavior-based robotic architectures allowing both fast reaction and deliberation in realtime. CCL provides a powerful support for distributed problem solving based on the cost optimization mechanism, and combines the best of the world of declarative and imperative programming. 1.

This paper presents a novel model for resource bounded computation based on process algebras.

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We outline a theory of evolutionary computation using a formal model of evolutionary computation -- the Evolutionary Turing Machine -- which is introduced as the extension of the Turing Machine model. Evolutionary Turing Machines provide a better and a more complete model for evolutionary computing than conventional Turing Machines, algorithms, and Markov chains. The convergence and convergence rate are defined and investigated in terms of this new model. The sufficient conditions needed for the completeness and optimality of evolutionary search are investigated. In particular, the notion of the total optimality as an instance of the multiobjective optimization of the Universal Evolutionary Turing Machine is introduced. This provides an automatic way to deal with the intractability of evolutionary search by optimizing the quality of solutions and search costs simultaneously. Based on a new model a very flexible classification of optimization problem hardness for the evolutionary techniques is proposed.

Abstract –The advances of modeling and simulation as well as agent systems have been phenomenal. The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems are opening new frontiers for advancing the studies of the physical, natural, social, military, and information sciences and engineering. This survey paper provides a comprehensive analysis of the evolution of the synergy of modeling and simulation and agent systems and their applications to various fields. The analysis framework explores the evolution of the application and technology of agent-directed simulation. We present a unified and comprehensive perspective regarding the broad range for the synergy of simulation and agents. In drawing the trace of the evolution of agents and simulation to a close, we offer comments on three aspects. The first involves the breadth and extent of agent simulation applications; the second relates to the role of agent simulation in computational experimentation and exploratory analysis; and the third pertains to the future of the use of agents in simulation, as well as simulation for agents. Index Terms – agent-directed simulation, agent simulation, agentbased simulation, agent-supported simulation, agent technology 1.1 Background 1.

The decision theory is defined typically as the combination of utility theory and probability theory. In this paper we generalize the decision theory as the performance measure theory and uncertainty theory. Intelligent