Francesco Percassi, Alfonso E. Gerevini, Enrico Scala, Ivan Serina, Mauro Vallati |
PosterID:
7
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| This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a search policy that exploits such a prediction in a state-space planner. Our policy combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the policy also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema. | |
| Canb | 10/27/2020, 18:00 – 19:00 |
| 10/31/2020, 01:00 – 02:00 |
| Paris | 10/27/2020, 08:00 – 09:00 |
| 10/30/2020, 15:00 – 16:00 |
| NYC | 10/27/2020, 03:00 – 04:00 |
| 10/30/2020, 10:00 – 11:00 |
| LA | 10/27/2020, 00:00 – 01:00 |
| 10/30/2020, 07:00 – 08:00 |
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