This paper introduces a combination of regression and belief revision to allow agents to deal with inconsistencies while executing plans. 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 and supports arbitrary sequences of actions. The paper illustrates how logic programming can be effectively used to support these processes.
Combining Intentionality and Belief: Revisiting Believable Character Plans
In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Proceedings of the 14th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment
- Page Range or eLocation-ID:
- 222 - 228
- Sponsoring Org:
- National Science Foundation
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