In this paper we present some preliminary concepts on extending Yokoo et al's distributed constraint satisfaction algorithms. Asynchronous backtracking and asynchronous weak-commitment search and all their improvements use a pure extensional constraint representation. All constraints

This article presents a distributed solution to autonomous quality of service provision in cognitive radio networks. Specifically, cognitive STDMA and CDMA communication networks are studied. Based on asynchronous weak commitment search the task of QoS provision is distributed among different

algorithm for solving Distributed CSPs called asynchronous weak-commitment search, which is inspired by the weak-commitment search algorithm for solving CSPs. This algorithm can revise a bad decision without an exhaustive search by changing the priority order of agents dynamically. Furthermore, agents can

of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental

and several MAS application problems of distributed CSPs. Furthermore, we describe a series of algorithms for solving distributed CSPs, i.e., the asynchronous backtracking, the asynchronous weak-commitment search, the distributed breakout, and distributed consistency algorithms. Finally,we showtwo extensions

these algorithms to handle multiple local variables, obtained algorithms are neither efficient nor scalable to larger problems. We develop a new algorithm that can handle multiple local variables efficiently, which is based on the asynchronous weak-commitment search algorithm. In this algorithm, a bad local

simulator, which allows us to model the impact of di#erent network tra#c conditions on the performance of the algorithms. We consider two complete DisCSP algorithms: asynchronous backtracking (ABT) and asynchronous weak commitment search (AWC).

We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constraint satisfaction algorithms and can efficiently make effective nogoods. We combine the method with the asynchronous weak-commitment

(e.g., forward checking) presuppose the existence of a consistent partial solution. Therefore, these heuristics can not be applied to iterative improvement algorithms. Furthermore, these algorithms are not theoretically complete. In this paper, a new algorithm called weak-commitment search which

volumes, ii Compatible file, device, and inter-process I/O, iii The ability to initiate asynchronous processes, iv System command language selectable on a per-user basis, v Over 100 subsystems including a dozen languages, vi High degree of portability. This paper discusses the nature and implementation