Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
A heuristic search approach to planning with temporally extended preferences
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
| Hi-index | 12.05 |
Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent's preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.