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Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.
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HELP! Providing Proactive Support in the Presence of Knowledge Asymmetry
While the development of proactive personal assistants has been a popular topic within AI research, most research in this direction tends to focus on a small subset of possible interaction settings. An important setting that is often overlooked is one where the users may have an incomplete or incorrect understanding of the task. This could lead to the user following incorrect plans with potentially disastrous consequences. Supporting such settings requires agents that are able to detect when the user's actions might be leading them to a possibly undesirable state and, if they are, intervene so the user can correct their course of actions. For the former problem, we introduce a novel planning compilation that transforms the task of estimating the likelihood of task failures into a probabilistic goal recognition problem. This allows us to leverage the existing goal recognition techniques to detect the likelihood of failure. For the intervention problem, we use model search algorithms to detect the set of minimal model updates that could help users identify valid plans. These identified model updates become the basis for agent intervention. We further extend the proposed approach by developing methods for pre-emptive interventions, to prevent the users from performing actions that might result in eventual plan failure. We show how we can identify such intervention points by using an efficient approximation of the true intervention problems, which are best represented as a Partially Observable Markov Decision-Process (POMDP). To substantiate our claims and demonstrate the applicability of our methodology, we have conducted exhaustive evaluations across a diverse range of planning benchmarks. These tests have consistently shown the robustness and adaptability of our approach, further solidifying its potential utility in real-world applications.
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- Award ID(s):
- 2303019
- PAR ID:
- 10537858
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704864
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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