Artificial Intelligence
Artificial Intelligence
Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
An axiomatic basis for computer programming
Communications of the ACM
Reasoning about Complex Actions with Incomplete Knowledge: A Modal Approach
ICTCS '01 Proceedings of the 7th Italian Conference on Theoretical Computer Science
Knowledge, action, and the frame problem
Artificial Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
FLUX: A logic programming method for reasoning agents
Theory and Practice of Logic Programming
A survey on knowledge compilation
AI Communications
Theory and Practice of Logic Programming
Computational Complexity: A Modern Approach
Computational Complexity: A Modern Approach
Soundness and completeness theorems for three formalizations of action
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Progression of situation calculus action theories with incomplete information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Action representation and partially observable planning using epistemic logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Verification of Sequential and Concurrent Programs
Verification of Sequential and Concurrent Programs
Understanding planning with incomplete information and sensing
Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Conditional planning with external functions
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
The frame problem and knowledge-producing actions
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Logic and AI in China: An Introduction
Minds and Machines
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Planning with incomplete knowledge becomes a very active research area since late 1990s. Many logical formalisms introduce sensing actions and conditional plans to address the problem. The action language $$\mathcal{A}_{K}$$ invented by Son and Baral is a well-known framework for this purpose. In this paper, we propose so-called cautious and weakly cautious semantics for $$\mathcal{A}_{K}$$ , in order to allow an agent to generate and execute reliable plans in safety-critical environments. Intuitively speaking, cautious and weakly cautious semantics enable the agent to know exactly what happens after the execution of an action. Computational complexity analysis shows that cautious semantics reduces the reasoning complexity of $$\mathcal{A}_{K}$$ , it is also worth to point out that many useful domains could still be expressed with this setting. Another important contribution of our work is the development of Hoare style proof systems. These proof systems are served as inference mechanisms for the verification of conditional plans, and proved to be sound and complete. In addition, they could also be used for plan generation, in the sense that constructing a derivation is indeed a procedure to finding a plan. We point out that the proof systems posses a nice property for off-line planning, that is, the agent could generate and store short proofs in her spare time, and perform quick plan query by easily constructing a long proof from the stored shorter ones (under the assumption that sufficient proofs are stored).