Explanation-based Learning System Research Articles

Inductive learning of search control rules for planning

One method for reducing the time required for plan generation is to learn search control rules from experience. The most common approach to learning search control knowledge has been explanationbased learning. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory and has the potential to create more effective rules by learning from more than one example at a time. In this paper we describe Grasshopper, an inductive system that learns search control rules for a classical plan generation system. We also provide an empirical evaluation of Grasshopper by comparing it with an existing explanation-based learning system.

Acquiring search-control knowledge via static analysis

Explanation-based learning (EBL) is a widely-used technique for acquiring search-control knowledge. Prieditis, van Harmelen, and Bundy pointed to the similarity between partial evaluation (PE) and EBL. However, EBL utilizes training examples whereas PE does not. It is natural to inquire, therefore, whether PE can be used to acquire search-control knowledge and, if so, at what cost? This paper answers these questions by means of a case study comparing prodigy/ebl, a state-of-the-art EBL system, and static, a PE-based analyzer of problem space definitions. When tested in prodigy/ebl's benchmark problem spaces, static generated search-control knowledge that was up to three times as effective as the knowledge learned by prodigy/ebl, and did so from twenty-six to seventy-seven times faster. The paper describes static's algorithms, compares its performance to prodigy/ebl's, noting when static's superior performance will scale up and when it will not. The paper concludes with several lessons for the design of EBL systems, suggesting hybrid PE/EBL systems as a promising direction for future research.

A structural theory of explanation-based learning

The impact of explanation-based learning (EBL) on problem-solving efficiency varies greatly from one problem space to another. In fact, seemingly minute modifications to problem space encoding can drastically alter EBL's impact. For example, while prodigy/ebl (a state-of-the-art EBL system) significantly speeds up the prodigy problem solver in the Blocksworld, prodigy/ebl actually slows prodigy down in a representational variant of the Blocksworld constructed by adding a single, carefully chosen, macro-operator to the Blocksworld operator set. Although EBL has been tested experimentally, no theory has been put forth that accounts for such phenomena. This paper presents such a theory. The theory exhibits a correspondence between a graph representation of problem spaces and the proofs used by EBL systems to generate search-control knowledge. The theory relies on this correspondence to account for the variations in EBL's impact. This account is validated by STATIC, a program that extracts EBL-style control knowledge directly from the graph representation, without using training examples. When tested on prodigy/ebl's benchmark tasks, static was up to three times as effective as prodigy/ebl in speeding up prodigy.

Finding optimal derivation strategies in redundant knowledge bases

A backward chaining process uses a collection of rules to reduce a given goal to a sequence of database retrievals. A “derivation strategy” is an ordering on these steps, specifying when to use each rule and when to perform each retrieval. Given the costs of reductions and retrievals, and the a priori likelihood that each particular retrieval will succeed, one can compute the expected cost of any strategy, for answering a specific query from a given knowledge base. Smith [19] presents an algorithm that finds the minimal cost strategy in time (essentially) linear in the number of rules, for any disjunctive, irredundant knowledge base. This paper proves that the addition of redundancies renders this task NP-hard. Many Explanation-Based Learning systems work by adding in redundancies; this shows the complexities inherent in their task.

Explanation-based learning:A problem solving perspective

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the prodigy problem solving system. prodigy's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. prodigy's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.

Explanation-based learning: a survey of programs and perspectives

Explanation-based learning (EBL) is a technique by which an intelligent system can learn by observing examples. EBL systems are characterized by the ability to create justified generalizations from single training instances. They are also distinguished by their reliance on background knowledge of the domain under study. Although EBL is usually viewed as a method for performing generalization, it can be viewed in other ways as well. In particular, EBL can be seen as a method that performs four different learning tasks: generalization, chunking, operationalization, and analogy. This paper provides a general introduction to the field of explanation-based learning. Considerable emphasis is placed on showing how EBL combines the four learning tasks mentioned above. The paper begins with a presentation of an intuitive example of the EBL technique. Subsequently EBL is placed in its historical context and the relation between EBL and other areas of machine learning is described. The major part of this paper is a survey of selected EBL programs, which have been chosen to show how EBL manifests each of the four learning tasks. Attempts to formalize the EBL technique are also briefly discussed. The paper concludes with a discussion of the limitations of EBL and the major open questions in the field.