A logic-based calculus of events
New Generation Computing
Fast planning through planning graph analysis
Artificial Intelligence
Event Calculus Planning Revisited
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Knowledge, action, and the frame problem
Artificial Intelligence
The ramification problem in the event calculus
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
The KGP model of agency for global computing: computational model and prototype implementation
GC'04 Proceedings of the 2004 IST/FET international conference on Global Computing
Multi-agent Cooperative Planning and Information Gathering
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Interleaving belief updating and reasoning in abductive logic programming
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Computational logic foundations of KGP agents
Journal of Artificial Intelligence Research
Theory and Practice of Logic Programming
Ambient intelligence: A survey
ACM Computing Surveys (CSUR)
Using the KGP model of agency to design applications
CLIMA'05 Proceedings of the 6th international conference on Computational Logic in Multi-Agent Systems
Logic Programs, Norms and Action
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In recent years, within the planning literature there has been a departure from approaches computing total plans for given goals, in favour of approaches computing partial plans. Total plans can be seen as (partially ordered) sets of actions which, if executed successfully, would lead to the achievement of the goals. Partial plans, instead, can be seen as (partially ordered) sets of actions which, if executed successfully, would contribute to the achievement of the goals, subject to the achievement of further sub-goals. Planning partially (namely computing partial plans for goals) is useful (or even necessary) for a number of reasons: (i) because the planning agent is resource-bounded, (ii) because the agent has incomplete and possibly incorrect knowledge of the environment in which it is situated, (iii) because this environment is highly dynamic. In this paper, we propose a framework to design situated agents capable of planning partially. The framework is based upon the specification of planning problems via an abductive variant of the event calculus.