Foundations of logic programming
Foundations of logic programming
Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Abstraction using generalization functions
Proc. of the 8th international conference on Automated deduction
Planning as search: a quantitative approach
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Abstraction in planning
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Abstracting operators for hierarchical planning
Proceedings of the first international conference on Artificial intelligence planning systems
Artificial Intelligence
A structural theory of explanation-based learning
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Case-based reasoning
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Automatically generating abstractions for planning
Artificial Intelligence
Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Abstracting Background Knowledge for Concept Learning
EWSL '91 Proceedings of the European Working Session on Machine Learning
Prodigy/Analogy: Analogical Reasoning in General Problem Solving
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Knowledge Acquisition by Generating Skeletal Plans from Real World Cases
Proceedings of the First Joint Workshop on Contemporary Knowledge Engineering and Cognition
Integrated Learning Architectures
ECML '93 Proceedings of the European Conference on Machine Learning
GWAI '92 Proceedings of the 16th German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Case-Based Reasoning and Expert System Development
Proceedings of the First Joint Workshop on Contemporary Knowledge Engineering and Cognition
Automated Acquisition of Control Knowledge to Improve the Quality of Plans
Automated Acquisition of Control Knowledge to Improve the Quality of Plans
PRODIGY 4.0: The Manual and Tutorial
PRODIGY 4.0: The Manual and Tutorial
Retrieval and organizational strategies in conceptual memory: a computer model
Retrieval and organizational strategies in conceptual memory: a computer model
Human Problem Solving
A Survey on Case-Based Planning
Artificial Intelligence Review
Conversational Case-Based Reasoning
Applied Intelligence
IEEE Transactions on Knowledge and Data Engineering
Semantic Abstractions in the Multimedia Domain
IEEE Transactions on Knowledge and Data Engineering
Hybrid Hierarchical Knowledge Organization for Planning
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Handling Cases and the Coverage in a Limited Quantity of Memory for Case-Based Planning Systems
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
The Case-Based Neural Network Model and Its Use in Medical Expert Systems
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Case-Based Reasoning for Breast Cancer Treatment Decision Helping
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Integrating Conversational Case Retrieval with generative Planning
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Local Predictions for Case-Based Plan Recognition
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Using Guidelines to Constrain Interactive Case-Based HTN Planning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Towards a Unified Theory of Adaption in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Knowledge-Level Task Model of Adaption in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
An Accurate Adaptation-Guided Similarity Metric for Case-Based Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Multiple-Domain Evaluation of Stratified Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Storing and indexing plan derivations through explanation-based analysis of retrieval failures
Journal of Artificial Intelligence Research
SiN: integrating case-based reasoning with task decomposition
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
On the role of the cases in case-based planning
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
An investigation of generalized cases
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
CBM-Gen+: an algorithm for reducing case base inconsistencies in hierarchical and incomplete domains
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
Kernel functions for case-based planning
Artificial Intelligence
Amalgams: a formal approach for combining multiple case solutions
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
On-the-Fly adaptive planning for game-based learning
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
ADAPT: abstraction hierarchies to better simulate teamwork under dynamics
AEGS'11 Proceedings of the 2011 international conference on Agents for Educational Games and Simulations
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Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description - as used in most hierarchical planners - has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of PARIS (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.