By Ahmad Afsahi, Nikitas J. Dimopoulos (auth.), Bo Kågström, Jack Dongarra, Erik Elmroth, Jerzy Waśniewski (eds.)
This e-book constitutes the conscientiously refereed court cases of the 4th foreign Workshop on utilized Parallel Computing, PARA'98, held in Umea, Sweden, in June 1998.
The seventy five revised papers awarded have been conscientiously reviewed and chosen for inclusion within the e-book. The papers tackle various issues in huge scale medical and industrial-strength computing, particularly high-performance computing and networking; instruments, languages, and environments for parallel processing; clinical visualization and digital fact; and destiny instructions in high-performance computing and communication.
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Additional resources for Applied Parallel Computing Large Scale Scientific and Industrial Problems: 4th International Workshop, PARA’98 Umeå, Sweden, June 14–17, 1998 Proceedings
The detailed mechanism, except for the action selection (acceptance or counteroffer), is described later. The action selection of RL in this paper is based on the method, which selects an action of the maximum worth (Q-value) at the probability, while selecting an action randomly at the probability. Fig. 3. RL-based agents As a concrete negotiation process, agents proceed as follows. Defining as the offer or threshold value of agent or as the same as starts with the first offer Then, ES- and LCS-based agents, agent accepts the offer if the acceptance (A) in the row of a matrix is determined from the same row selected; otherwise, it makes a counter-offer Towards Verification and Validation in Multiagent-Based Systems 33 This cycle is continued until either agent accepts the offer of the other agent or the maximum number of steps (MAX_STEP) is exceeded.
Thus, starting from a single CM and following this engineering process, several computational models (implementations) can be elaborated. Consequently, very different outputs may be obtained and the question of ABS experiments’ reliability must be raised. Recent works such as [5,6] clearly address this matter. In this paper, this problem is identified as the engineering divergence phenomenon. It is important to distinguish this phenomenon, observed at the engineering phases, from the divergence of a particular CM due to its inherent properties.
1068–1094, 1991. 22. : “Perfect Equilibrium in a Bargaining Model,” Econometrica, Vol. 50, No. 1, pp. 97–109, 1982. 23. Smith, S. : “Flexible Learning of Problem Solving Heuristics through Adaptive Search,” The 8th International Joint Conference on Artificial Intelligence (IJCAI ’83), pp. 422–425, 1983. 24. : Bargaining Theory, Economics Research Institute at the Stockholm School of Economics, 1972. 25. Sutton, R. S. and Bart, A. : Reinforcement Learning – An Introduction –, The MIT Press, 1998.
Applied Parallel Computing Large Scale Scientific and Industrial Problems: 4th International Workshop, PARA’98 Umeå, Sweden, June 14–17, 1998 Proceedings by Ahmad Afsahi, Nikitas J. Dimopoulos (auth.), Bo Kågström, Jack Dongarra, Erik Elmroth, Jerzy Waśniewski (eds.)