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Algorithmic Decision Theory: Second International by Dorothea Baumeister, Gábor Erdélyi, Jörg Rothe (auth.),

By Dorothea Baumeister, Gábor Erdélyi, Jörg Rothe (auth.), Ronen I. Brafman, Fred S. Roberts, Alexis Tsoukià s (eds.)

This ebook constitutes the refereed lawsuits of the second one foreign convention on Algorithmic selection idea, ADT 2011, held in Piscataway, NJ, united states, in October 2011. The 24 revised complete papers provided have been conscientiously reviewed and chosen from 50 submissions.

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Extra info for Algorithmic Decision Theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings

Example text

Assuming that sub-goals are probabilistically independent, then, in each stage of the planning process P rgoal holds the accurate probability of the goal state. Proof. If G = {L} then it’s immediate from lemma 2. Otherwise, from lemma 2 it follows that this holds true for every sub-goal. Thus, the probability of the goal is the product of the probability of the sub-goals. I. Brafman and R. Taig fact that we initialize P rgoal correctly, and from the updates performed following each action. Specifically, suppose that the probability of subgoal g increased following the old last action.

Then, we discuss our system and its empirical performance, evaluating it against PFF on standard CPP domains. Finally, we discuss some extensions. 1 Conformant Probabilistic Planning The probabilistic planning framework we consider adds probabilistic uncertainty to a subset of the classical ADL language, namely (sequential) STRIPS with conditional effects. Such STRIPS planning tasks are described over a set of propositions P as triples (A, I, G), corresponding to the action set, initial world state, and goals.

Note that these can be computed from the initial state description. I. Brafman and R. , the initial probability that p holds. – P rgoal = bI ({s|s |= G}). Again, this can be computed directly from the initial state description. Goal: G = {P rgoal ≥ θ}. ˆ we make all its effects conditionals. Thus, if e Actions: First, for every action a ∈ A, is an effect of a, we now treat it as a conditional effect of the form ∅ → {e}. For every action a ∈ A, Aˆ contains an action a defined as follows: – pre(a) = {Pl = 1 | l ∈ pre(a)}.

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