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JAIR is pleased to announce the publication of the following article:
E. Amir and A. Chang (2008) "Learning Partially Observable Deterministic Action
Models", Volume 33, pages 349-402
For quick access via your WWW browser, use this URL:
http://www.jair.org/papers/paper2575.html
Abstract:
We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains.
Our algorithms take sequences of partial observations over time as input, and
output deterministic action models that could have lead to those observations.
The algorithms output all or one of those models (depending on our choice), and
are exact in that no model is misclassified given the observations. Our
algorithms take polynomial time in the number of time steps and state features
for some traditional action classes examined in the AI-planning literature,
e.g., STRIPS actions. In contrast, traditional approaches for HMMs and
Reinforcement Learning are inexact and exponentially intractable for such
domains. Our experiments verify the theoretical tractability guarantees, and
show that we identify action models exactly. Several applications in planning,
autonomous exploration, and adventure-game playing already use these results.
They are also promising for probabilistic settings, partially observable
reinforcement learning, and diagnosis.
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