JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Abduction in well-founded semantics and generalized stable models via tabled dual programs
Theory and Practice of Logic Programming
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
The logical way to be artificially intelligent
CLIMA'05 Proceedings of the 6th international conference on Computational Logic in Multi-Agent Systems
Elder care via intention recognition and evolution prospection
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
Intention-based decision making with evolution prospection
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Knowledge representation language p-log --- a short introduction
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Moral reasoning under uncertainty
LPAR'12 Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
The emergence of commitments and cooperation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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This work concerns the problem of modelling evolving prospective agent systems. Inasmuch a prospective agent [1] looks ahead a number of steps into the future, it is confronted with the problem of having several different possible courses of evolution, and therefore needs to be able to prefer amongst them to decide the best to follow as seen from its present state. First it needds a priori preferences for the generation of likely courses of evolution. Subsequently, this being one main contribution of this paper, based on the historical information as well as on a mixture of quantitative and qualitative a posteriori evaluation of its possible evolutions, we equip our agent with so-called evolution-level preferences mechanism, involving three distinct types of commitment. In addition, one other main contribution, to enable such a prospective agent to evolve, we provide a way for modelling its evolving knowledge base, including environment and course of evolution triggering of all active goals (desires), context-sensitive preferences and integrity constraints. We exhibit several examples to illustrate the proposed concepts.