1,721,080 research outputs found
Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning
Full text linkCase-Based planning can fruitfully exploit knowledge
gained by solving a large number of problems, storing
the corresponding solutions in a plan library and reusing
them for solving similar planning problems in the future.
Case-based planning is extremely effective when
similar reuse candidates can be efficiently chosen.
In this paper, we study an innovative technique based
on planning problem features for efficiently retrieving
solved planning problems (and relative plans) from
large plan libraries. A problem feature is a characteristic
of the instance that can be automatically derived from
the problem specification, domain and search space
analyses, and different problem encodings.
Since the use of existing planning features are not always
able to effectively distinguish between problems
within the same planning domain, we introduce a new
class of features.
An experimental analysis in this paper shows that our
features-based retrieval approach can significantly improve
the performance of a state-of-the-art case-based
planning system
Discovering state constraints for planning with conditional effects in Discoplan (part I)
No full textAutomated Planning in Temporal Domains: Some Reecent Advances and Current Research Topics
No full textIncremental Qualitative Temporal Reasoning: Algorithms for the Point Algebra and the ORD-Horn Class
No full textIn many applications of temporal reasoning we are interested in processing temporal information
incrementally. In particular, given a set of temporal constraints (a temporal CSP) and a new
constraint, we want to maintain certain properties of the extended temporal CSP (e.g., a solution),
rather than recomputing them from scratch. The Point Algebra (PA) and the Interval Algebra (IA)
are two well-known frameworks for qualitative temporal reasoning. The reasoning algorithms for PA
and the tractable fragments of IA, such as Nebel and Bürckert’s maximal tractable class of relations
(ORD-Horn), have originally been designed for “static” reasoning.
In this paper, we study the incremental version of the fundamental reasoning problems in the
context of these tractable classes. We propose a collection of new polynomial algorithms that can
amortize their complexity when processing a sequence of input constraints to incrementally decide
satisfiability, to maintain a solution, or to update the minimal representation of the CSP. Our incremental
algorithms improve the total time complexity of using existing static techniques by a factor of
O(n) or O(n^2), where n is the number of the variables involved by the temporal CSP. An experimental
analysis focused on constraints over PA confirms the computational advantage of our incremental
approach
Planning as Propositional CSP: From Walksat to Local Search on Planning Graphs with Action Costs
No full text
Qualitative Spatio-Temporal Reasoning with RCC-8 and Allen's Interval Calculus: Computational Complexity
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