The management of context-sensitive features: A review of strategies

In this paper, we review five heuristic strategies for handling context- sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context-sensitive learning

The identification of context-sensitive features: A formal definition of context for concept learning

A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi- dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant features. We also formally define what it means for one feature to be context-sensitive to another feature. Context-sensitive features complicate the task of the learner and potentially impair the learner's performance. Our formal definitions make it possible for a learner to automatically identify context-sensitive features. After context-sensitive features have been identified, there are several strategies that the learner can employ for managing the features; however, a discussion of these strategies is outside of the scope of this paper. The formal definitions presented here correct a flaw in previously proposed definitions. We discuss the relationship between our work and a formal definition of relevance

Feature selection algorithms: a survey and experimental evaluation

In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several fundamental algorithms found in the literature and assesses their performance in a controlled scenario. A scoring measure ranks the algorithms by taking into account the amount of relevance, irrelevance and redundance on sample data sets. This measure computes the degree of matching between the output given by the algorithm and the known optimal solution. Sample size effects are also studied.Postprint (published version

VAL : automatic plan validation, continuous effects and mixed initiative planning using PDDL

This paper describes aspects of our plan validation tool, VAL. The tool was initially developed to support the 3rd International Planning Competition, but has subsequently been extended in order to exploit its capabilities in plan validation and development. In particular, the tool has been extended to include advanced features of PDDL2.1 which have proved important in mixed-initiative planning in a space operations project. Amongst these features, treatment of continuous effects is the most significant, with important effects on the semantic interpretation of plans. The tool has also been extended to keep abreast of developments in PDDL, providing critical support to participants and organisers of the 4th IPC

Constructing Conditional Plans by a Theorem-Prover

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface