This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts
The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-l...
This book mainly aims at solving the problems in both cooperative and competitive multi-agent system...
This thesis addresses the problem of collective, iterative allocation which is a challenging problem...
Complex systems generally have many components and it is difficult to understand the whole system on...
The complex systems can be best dealt by decomposing them into subsystems or Multi-Agent System (MAS...
Multi-agent coordination problems can be cast as distributed optimization tasks. Probability collect...
International audienceIn this paper, a multi-agent probabilistic optimization algorithm is applied t...
Increasingly powerful computers are making possible distributed systems comprised of many adaptive a...
These transactions publish research in computer-based methods of computational collective intelligen...
Abstract. We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within ...
In this paper, we consider optimization problems involving multiple agents. Each agent introduces it...
Distributed artificial intelligence (DAI) and multi-agent system (MAS) has recently gained increasin...
This book consists of 20 chapters in which the authors deal with different theoretical and practical...
The book consists of 19 extended and revised chapters based on original works presented during a pos...
In this paper we study the COllective INtelligence (COIN) framework of Wolpert et al. for dispersion...
The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-l...
This book mainly aims at solving the problems in both cooperative and competitive multi-agent system...
This thesis addresses the problem of collective, iterative allocation which is a challenging problem...
Complex systems generally have many components and it is difficult to understand the whole system on...
The complex systems can be best dealt by decomposing them into subsystems or Multi-Agent System (MAS...
Multi-agent coordination problems can be cast as distributed optimization tasks. Probability collect...
International audienceIn this paper, a multi-agent probabilistic optimization algorithm is applied t...
Increasingly powerful computers are making possible distributed systems comprised of many adaptive a...
These transactions publish research in computer-based methods of computational collective intelligen...
Abstract. We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within ...
In this paper, we consider optimization problems involving multiple agents. Each agent introduces it...
Distributed artificial intelligence (DAI) and multi-agent system (MAS) has recently gained increasin...
This book consists of 20 chapters in which the authors deal with different theoretical and practical...
The book consists of 19 extended and revised chapters based on original works presented during a pos...
In this paper we study the COllective INtelligence (COIN) framework of Wolpert et al. for dispersion...
The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-l...
This book mainly aims at solving the problems in both cooperative and competitive multi-agent system...
This thesis addresses the problem of collective, iterative allocation which is a challenging problem...