Computer networks: a systems approach
Computer networks: a systems approach
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Software Agents for Future Communication Systems
Software Agents for Future Communication Systems
Learning Situation-Specific Coordination in Cooperative Multi-agent Systems
Autonomous Agents and Multi-Agent Systems
On Augmenting Reactivity with Deliberation in a Controlled Manner
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Representation of procedural knowledge of an intelligent agent using a novel cognitive memory model
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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This paper proposes an agent-architecture to deal with real-time problems where it is important both to react to constant changes in the state of the environment and to recognize the generic tendencies in the sequence of those changes. Reactivity must satisfy the need for immediate answers; cognition will enable the perception of medium and long time variations, allowing decisions that lead to an improved reactivity. Agents are able to evolve through an instance-based learning mechanism fed by the cognition process that allows them to improve their performance as they accumulate experience. Progressively, they learn to relate their ways of reacting (reaction strategies) with the general state of the environment. Using a simulation workbench that sets a distributed communication problem, different tests are made in an effort to validate our proposal and put it in perspective as a solution for other problems.