Add to ORKGThe problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision process (POMDP)
Coordination of distributed entities is required for problems arising in many areas, including multi...
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solv...
In this paper we focus on distributed multiagent planning under uncertainty. For single-agent planni...
While formal, decision-theoretic models such as the Markov Decision Process (MDP) have greatly advan...
Rapport interne.Defining the behaviour of a set of situated agents, such that a collaborative proble...
Colloque sans acte à diffusion restreinte. internationale.International audienceDefining the behavio...
Decentralized policies for information gathering are required when multiple autonomous agents are de...
Recent years have seen significant advances in techniques for op-timally solving multiagent problems...
Recent years have seen significant advances in techniques for optimally solving multiagent problems ...
Research in autonomous agent planning is gradually mov-ing from single-agent environments to those p...
Nous abordons dans cette thèse la résolution optimale des processus de décision markoviens décentral...
© 2019 AI Access Foundation. All rights reserved. Decentralized partially observable Markov decision...
The subject of this thesis is the optimal resolution of decentralized Markov decision processes (DEC...
The subject of this thesis is the optimal resolution of decentralized Markov decision processes (DEC...
International audienceRecent years have seen significant advances in techniques for optimally solvin...
Coordination of distributed entities is required for problems arising in many areas, including multi...
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solv...
In this paper we focus on distributed multiagent planning under uncertainty. For single-agent planni...
While formal, decision-theoretic models such as the Markov Decision Process (MDP) have greatly advan...
Rapport interne.Defining the behaviour of a set of situated agents, such that a collaborative proble...
Colloque sans acte à diffusion restreinte. internationale.International audienceDefining the behavio...
Decentralized policies for information gathering are required when multiple autonomous agents are de...
Recent years have seen significant advances in techniques for op-timally solving multiagent problems...
Recent years have seen significant advances in techniques for optimally solving multiagent problems ...
Research in autonomous agent planning is gradually mov-ing from single-agent environments to those p...
Nous abordons dans cette thèse la résolution optimale des processus de décision markoviens décentral...
© 2019 AI Access Foundation. All rights reserved. Decentralized partially observable Markov decision...
The subject of this thesis is the optimal resolution of decentralized Markov decision processes (DEC...
The subject of this thesis is the optimal resolution of decentralized Markov decision processes (DEC...
International audienceRecent years have seen significant advances in techniques for optimally solvin...
Coordination of distributed entities is required for problems arising in many areas, including multi...
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solv...
In this paper we focus on distributed multiagent planning under uncertainty. For single-agent planni...