Pleasure: the answer to conflicting motivations
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
Commitment and effectiveness of situated agents
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Improving Choice Mechanisms within the BVG Architecture
ATAL '00 Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages
Centralized Regulation of Social Exchanges Between Personality-Based Agents
Coordination, Organizations, Institutions, and Norms in Agent Systems II
Regulating social exchanges between personality-based non-transparent agents
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Analysing partner selection through exchange values
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
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Even a simple artificial agent will have to make decisions and consider their consequences. An agent can achieve this by evaluating situations along several dimensions. The BVG (Beliefs, Values and Goals) architecture is a means to build up such an evaluation machinery. The motivational part of the agent's mind is captured by the concept of value, able to capture the preferences of the agent, which cannot be reduced to either beliefs or goals, or a combination of the two. We sketch a calculus for agent decision, and look into the problem of feedbacking the consequences of actions into the agent's decision mechanism, in order to adjust its behaviour for future occasions. Finally, we describe an experiment where these ideas are put to test and look into its results to draw conclusions about their general applicability.