About the research
Introduction
TODO
Tip: How to insert objects like this "Table of contents" into your page.
Plan Recognition As Planning
In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G* of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G* if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G* can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G* while scaling up as well as state-of-the-art planning algorithms and heuristics.
Publications
Plan Recognition As Planning
Ramirez, M. and Geffner, H.
Published on Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09)
2009, Pasadena, California, USA
BibTeX entry:
@inproceedings{ RamirezGeffner09,
author = { M. Ramirez and H. Geffner },
title = {Plan Recognition As Planning},
booktitle = { Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09)},
year = {2009} }
Probabilistic Plan Recognition As Planning
Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach.
Publications
Probabilistic Plan Recognition Using Off-The-Shelf Classical Planners
Ramirez, M. and Geffner, H.
Published on Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)
2010, Atlanta, Georgia, USA
BibTeX entry:
@inproceedings{ RamirezGeffner10,
author = { M. Ramirez and H. Geffner },
title = {Probabilistic Plan Recognition Using Off-The-Shelf Classical Planners},
booktitle = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)},
year = {2010} }