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» Learning Initial Trust Among Interacting Agents
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AAMAS
2005
Springer
13 years 7 months ago
Learning and Exploiting Relative Weaknesses of Opponent Agents
Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of s...
Shaul Markovitch, Ronit Reger
AAAI
1997
13 years 9 months ago
Benefits of Learning in Negotiation
Negotiation has been extensively discussed in gametheoretic, economic, and management science literatures for decades. Recent growing interest in electronic commerce has given inc...
Dajun Zeng, Katia P. Sycara
ARGMAS
2006
Springer
13 years 11 months ago
Arguments and Counterexamples in Case-Based Joint Deliberation
Multiagent learning can be seen as applying ML techniques to the core issues of multiagent systems, like communication, coordination, and competition. In this paper, we address the...
Santiago Ontañón, Enric Plaza
JAIR
2006
111views more  JAIR 2006»
13 years 7 months ago
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning prob...
Vadim Bulitko, Greg Lee
ECAI
2008
Springer
13 years 9 months ago
Exploiting locality of interactions using a policy-gradient approach in multiagent learning
In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
Francisco S. Melo