Abstract
This paper explores the challenge of encountering incorrect beliefs in the context of reasoning about actions and changes using action languages with sensing actions. An incorrect belief occurs when some observations conflict with the agent’s own beliefs. A common approach to recover from this situation is to replace the initial beliefs with beliefs that conform to the sequence of actions and the observations. The paper introduces a regression-based and revision-based approach to calculate a correct initial belief. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws, supports arbitrary sequences of actions, and integrates belief revision methods to select a meaningful initial belief state among possible alternatives.
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Notes
- 1.
We ignore the possibility that some other agent turns on the light while the robot is moving to the kitchen. This could be identified with the first option.
- 2.
- 3.
The original definition by Dalal identifies the set of formulae which are true in \(\min (\varSigma _\varphi , \sqsubseteq _\psi )\).
- 4.
The results of the computation is the same if states are represented using only positive literals. In [14], \(\{\{\lnot Litmus, Litmus\}\}\) would be considered as \(\{\{Litmus\}\}\).
- 5.
Note we freely exchange between sets of literals and conjunctions of literals.
- 6.
- 7.
We omit the precise description to save space.
- 8.
E.g., all non-probabilistic domains in www.icaps-conference.org/competitions.
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Tardivo, F., Pham, L., Son, T.C., Pontelli, E. (2021). A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief States. In: Morales, J.F., Orchard, D. (eds) Practical Aspects of Declarative Languages. PADL 2021. Lecture Notes in Computer Science(), vol 12548. Springer, Cham. https://doi.org/10.1007/978-3-030-67438-0_5
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