17,090 research outputs found
Towards learning domain-independent planning heuristics
Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic approaches are necessary to solve all but the simplest problems. In
this work, we explore the possibility of obtaining domain-independent heuristic
functions using machine learning. This is a part of a wider research program
whose objective is to improve practical applicability of planning in systems
for which the planning domains evolve at run time. The challenge is therefore
the learning of (corrections of) domain-independent heuristics that can be
reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality
and Autonom
Marvin: A Heuristic Search Planner with Online Macro-Action Learning
This paper describes Marvin, a planner that competed in the Fourth
International Planning Competition (IPC 4). Marvin uses
action-sequence-memoisation techniques to generate macro-actions, which are
then used during search for a solution plan. We provide an overview of its
architecture and search behaviour, detailing the algorithms used. We also
empirically demonstrate the effectiveness of its features in various planning
domains; in particular, the effects on performance due to the use of
macro-actions, the novel features of its search behaviour, and the native
support of ADL and Derived Predicates
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
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
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