Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Tennenholtz, M."'
Autor:
Monderer, D., Tennenholtz, M.
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 21, pages 37-62, 2004
This paper discusses an interested party who wishes to influence the behavior of agents in a game (multi-agent interaction), which is not under his control. The interested party cannot design a new game, cannot enforce agents' behavior, cannot enforc
Externí odkaz:
http://arxiv.org/abs/1107.0022
Autor:
Brafman, R. I., Tennenholtz, M.
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 19, pages 11-23, 2003
In common-interest stochastic games all players receive an identical payoff. Players participating in such games must learn to coordinate with each other in order to receive the highest-possible value. A number of reinforcement learning algorithms ha
Externí odkaz:
http://arxiv.org/abs/1106.5258
Autor:
Tennenholtz, M.
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 17, pages 363-378, 2002
Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical game-theoretic equilibrium an
Externí odkaz:
http://arxiv.org/abs/1106.4570
Autor:
Monderer, D., Tennenholtz, M.
Publikováno v:
Journal of Artificial Intelligence Research, Vol 7, (1997), 231-248
The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change
Externí odkaz:
http://arxiv.org/abs/cs/9711104
Autor:
Brafman, R. I., Tennenholtz, M.
Publikováno v:
Journal of Artificial Intelligence Research, Vol 4, (1996), 477-507
Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and un
Externí odkaz:
http://arxiv.org/abs/cs/9606102
Publikováno v:
Journal of Artificial Intelligence Research, Vol 2, (1995), 475-500
We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive
Externí odkaz:
http://arxiv.org/abs/cs/9505102
Autor:
Safra, S., Tennenholtz, M.
Publikováno v:
Journal of Artificial Intelligence Research, Vol 2, (1994), 111-129
This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in an environment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that
Externí odkaz:
http://arxiv.org/abs/cs/9409101
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