A mechanical solution of Schubert's steamroller by many-sorted resolution
Artificial Intelligence
Planning for conjunctive goals
Artificial Intelligence
Many sorted logic=unsorted logic+control?
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Similarity and analogical reasoning
Similarity and analogical reasoning
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
The mechanisms of analogical learning
Readings in knowledge acquisition and learning
The computational complexity of propositional STRIPS planning
Artificial Intelligence
On point-based temporal disjointness
Artificial Intelligence
Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Survey on Case-Based Planning
Artificial Intelligence Review
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Using Many-Sorted Logic in the Object-Oriented Data Model for Fast Robot Task Planning
Journal of Intelligent and Robotic Systems
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Weighting by Explaining Case-Based Planning Episodes
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
On Plan Adaption through Planning Graph Analysis
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Structure-Based Similarity Search with Graph Histograms
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Convolution Edit Kernel for Error-tolerant Graph Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Case-Based Plan Adaptation: An Analysis and Review
IEEE Intelligent Systems
Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs
Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
The detection and exploitation of symmetry in planning problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Domain independent approaches for finding diverse plans
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
An average case analysis of planning
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
An analysis on transformational analogy: general framework and complexity
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Qualitative vs. quantitative plan diversity in case-based planning
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
A case-based approach to heuristic planning
Applied Intelligence
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Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen. In this paper we describe an innovative case-based planning system, called OAKplan, which can efficiently retrieve planning cases from plan libraries containing more than ten thousand cases, choose heuristically a suitable candidate and adapt it to provide a good quality solution plan which is similar to the one retrieved from the case library. Given a planning problem we encode it as a compact graph structure, that we call Planning Encoding Graph, which gives us a detailed description of the topology of the planning problem. By using this graph representation, we examine an approximate retrieval procedure based on kernel functions that effectively match planning instances, achieving extremely good performance in standard benchmark domains. The experimental results point out the effect of the case base size and the importance of accurate matching functions for global system performance. Overall, we show that OAKplan is competitive with state-of-the-art plan generation systems in terms of number of problems solved, CPU time, plan difference values and plan quality when cases similar to the current planning problem are available in the plan library.