Explanation-based Learning Algorithms Research Articles

On the Application of Explanation-Based Learning to Acquire Control Knowledge for Branch and Bound Algorithms

The goal of this article is to present a methodology for the automatic acquisition of new control knowledge in the form of dominance and equivalence conditions, using an explanation-based learning algorithm from artificial intelligence. The acquisition of new control knowledge proceeds through the following stages: 1) Analysis of the problem-solving activity, generated from the application of a branch and bound algorithm on specific instance(s) of a class of problems. 2) Interpretation of the problem-solving activity and synthesis of new control knowledge, which can lead to more efficient solution of future problems. The generated control knowledge is provably correct within the theory of the given class of problems and the employed branch and bound strategy. Consequently, the number of nodes evaluated by a specific branch and bound algorithm is guaranteed to be reduced, as new control knowledge is continuously acquired from the solution of specific problems. The article presents the theoretical foundations for the handling of the above two tasks. It concludes with an application of the proposed approach to a mixed integer linear programming formulation of a batch scheduling problem.

A high-performance explanation-based learning algorithm

The main contribution of this paper is a new domain-independent explanation-based learning (EBL) algorithm. The new EBL∗DI algorithm significantly outperforms traditional EBL algorithms both by learning in situations where traditional algorithms cannot learn as well as by providing greater problem-solving performance improvement in general. The superiority of the EBL∗DI algorithm is demonstrated with experiments in three different application domains. The EBL∗DI algorithm is developed using a novel formal framework in which traditional EBL techniques are reconstructed as the structured application of three explanation-transformation operators. We extend this basic framework by introducing two additional operators that, when combined with the first three operators, allow us to prove a completeness result: in the formal framework, every EBL algorithm is equivalent to the application of the five transformation operators according to some control strategy. The EBL∗DI algorithm employs all five proof-transformation operators guided by five domain-independent control heuristics.

Creativity and learning in a case-based explainer

Explanation-based learning (EBL) is a very powerful method for category formation. Since EBL algorithms depend on having good explanations, it is crucial to have effective ways to build explanations, especially in complex real-world situations where complete causal information is not available. When people encounter new situations, they often explain them by remembering old explanations, and adapting them to fit. We believe that this case-based approach to explanation holds promise for use in AI systems, both for routine explanation and to creatively explain situations quite unlike what the system has encountered before. Building new explanations from old ones relies on having explanations available in memory. We describe explanation patterns (XPs), knowledge structures that package the reasoning underlying explanations. Using the SWALE system as a base, we discuss the retrieval and modification process, and the criteria used when deciding which explanation to accept. We also discuss issues in learning XPs: what generalization strategies are appropriate for real-world explanations, and which indexing strategies are appropriate for XPs. SWALE's explanations allow it to understand nonstandard stories, and the XPs it learns increase its efficiency in dealing with similar anomalies in the future.