25,212 research outputs found
FluCaP: A Heuristic Search Planner for First-Order MDPs
We present a heuristic search algorithm for solving first-order Markov
Decision Processes (FOMDPs). Our approach combines first-order state
abstraction that avoids evaluating states individually, and heuristic search
that avoids evaluating all states. Firstly, in contrast to existing systems,
which start with propositionalizing the FOMDP and then perform state
abstraction on its propositionalized version we apply state abstraction
directly on the FOMDP avoiding propositionalization. This kind of abstraction
is referred to as first-order state abstraction. Secondly, guided by an
admissible heuristic, the search is restricted to those states that are
reachable from the initial state. We demonstrate the usefulness of the above
techniques for solving FOMDPs with a system, referred to as FluCaP (formerly,
FCPlanner), that entered the probabilistic track of the 2004 International
Planning Competition (IPC2004) and demonstrated an advantage over other
planners on the problems represented in first-order terms
Engineering a Conformant Probabilistic Planner
We present a partial-order, conformant, probabilistic planner, Probapop which
competed in the blind track of the Probabilistic Planning Competition in IPC-4.
We explain how we adapt distance based heuristics for use with probabilistic
domains. Probapop also incorporates heuristics based on probability of success.
We explain the successes and difficulties encountered during the design and
implementation of Probapop
Planning in probabilistic domains using a deterministic numeric planner
In the probabilistic track of the IPC5 - the last International planning competitions - a probabilistic planner based on combining deterministic planning with replanning - FF-REPLAN - out performed the other competitors. This probabilistic planning paradigm discarded the probabilistic information of the domain, just considering for each action its nominal effect as a deterministic effect
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competitionās central role in shaping the landscape of ongoing
developments in evaluating planning systems
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