Technion, IEM faculty - Information Systems seminar

Speaker: Erez Karpas
Title: Non-classical Heuristics for Classical Planning
Date: 04/01/2012
Time: 13:00
Place: Bloomfield 527
Abstract:  <>
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Domain-independent planning is one of the foundational areas in the
field of Artificial Intelligence (AI). A domain-indpendent planning task
consists of an initial world state, a goal, and a set of actions for
modifying the world state, with the objective of finding a plan that
transforms the initial world state into a goal state. In cost-optimal
planning, we are interested in finding not just any valid plan, but a
cheapest such plan. One of the most prominent approaches to cost-optimal
planning these days is heuristic state-space search, guided by a
heuristic which estimates the distance from each state to the goal. Most
heuristics for domain-independent planning are what we call classical
--- they estimate the distance from some given state to the goal using
only properties of the given state. In this work, we explore
non-classical heuristics --- heuristics which exploit additional
information gathered during search. We propose a mathematical model
which allows us to formally define non-classical heuristics, as well as
a useful taxonomy of heuristics along several dimensions. We then
describe two different classes of non-classical heuristics:
landmark-based heuristics, and machine-learning based heuristics. Our
empirical evaluation shows that non-classical heuristics are not just an
interesting theoretical possibility, but rather state-of-the-art tools
in heuristic search planning.
Technion Math. Net (TECHMATH)
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