This paper defines precisely the notion of bias in generalization problems, then shows that biases are necessary for the inductive leap. Classes of justifiable biases are considered, and the relationship between bias and domain-independence is considered

This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially.

The unification problem and several variants are presented. Various algorithms and data structures are discussed. Research on unification arising in several areas of computer science is surveyed, these areas include theorem proving, logic programming, and natural language processing. Sections of the paper include examples that highlight particular uses

This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies. Besides describing briefly each of these approaches (or groups of techniques), we provide some criticism regarding their highlights and drawbacks. A small comparative study is also conducted, in order to assess the performance of several penalty-based approaches with respect to a dominance-based technique proposed by the author, and with respect to some mathematical programming approaches. Finally, we provide some guidelines regarding how to select the most appropriate constraint-handling technique for a certain application, ad we conclude with some of the the most promising paths of future research in this area.

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When Newell introduced the concept of the knowledge level as a useful level of description for computer systems, he focused on the representation of knowledge. This paper applies the knowledge level notion to the problem of knowledge acquisition. Two interesting issues arise. First, some existing machine learning programs appear to be completely static when viewed at the knowledge level. These programs improve their performance without changing their "knowledge." Second, the behavior of some other machine learning programs cannot be predicted or described at the knowledge level. These programs take unjustified inductive leaps. The first programs are called symbol level learning (SLL) programs; the second, non-deductive knowledge level learning (NKLL) programs. The paper analyzes both of these classes of learning programs and speculates on the possibility of developing coherent theories of each. A theory of symbol level learning is sketched, and some reasons are presented for believing...

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This paper shows how to integrate two complementary techniques for manipulating program invariants: dynamic detection and static verification. Dynamic detection proposes likely invariants based on program executions, but the resulting properties are not guaranteed to be true over all possible executions. Static verification checks that properties are always true, but it can be difficult and tedious to select a goal and to annotate programs for input to a static checker. Combining these techniques overcomes the weaknesses of each: dynamically detected invariants can annotate a program or provide goals for static verification, and static veri cation can confirm properties proposed by a dynamic tool. We have

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 extract...

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