Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

Integrating explanation-based and empirical learning methods in OCCAM

This paper discusses an approach to integrating empirical and explanation based learning techniques. The paper focuses on OCCAM, a program that has the capability to acquire via empirical means the knowledge needed for analytical learning. Two examples of this capability are discussed:The ability to use empirical techniques to acquire a domain theory for explanation based learning.The ability to use empirical learning techniques to find common patterns for causal relationships. These patterns encode a theory of causality (i.e., a set of general principles for recognizing causal relationships). Once acquired, a theory of causality can facilitate later learning by focusing on hypotheses which are consistent with the theory

Average case analysis of empirical and explanation-based learning algorithms

We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to mathematically model the performance of learning algorithms in order to better understand the nature of their empirical behavior. We are interested in how differences in learning algorithms influence the expected accuracy of the concepts learned.We present the Average Case Learning Model and apply the model to three learning algorithms: a purely empirical algorithm (Bruner's Wholist), an algorithm which prefers analytical (explanation-based) learning over empirical learning (EBL-FIRST-TM) and an algorithm integrating both analytical and empirical learning (lOSC-TM). The Average Case Learning Model is unique in that it is able to accurately predict the expected behavior of learning algorithms. We compare average case analysis to Valiant's Probably Approximately Correct (PAC) learning model

Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

The influence of prior knowledge on concept acquisition : experimental and computational results

The influence of the prior causal knowledge of subjects on the rate of learning, the categories formed, and the attributes attended to during learning is explored. Conjunctive concepts are thought to be easier for subjects to learn than disjunctive concepts. Conditions are reported under which the opposite occurs. In particular, it is demonstrated that prior knowledge can influence the rate of concept learning and that the influence of prior causal knowledge can dominate the influence of the logical form. A computational model of this learning task is presented. In order to represent the prior knowledge of the subjects, an extension to explanation-based learning is developed to deal with imprecise domain knowledge

An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

Automated revision of CLIPS rule-bases

This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases

Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202