Showing papers in "Artificial Intelligence in 1997"

TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.

TL;DR: This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area.

TL;DR: Three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem are described and compared, giving 89% correct predictions on a musk odor prediction task.

TL;DR: Graphplan as mentioned in this paper is a partial-order planner based on constructing and analyzing a compact structure called a planning graph, which can be used to find the shortest possible partial order plan or state that no valid plan exists.

TL;DR: An abstract framework for default reasoning, which includes Theorist, default logic, logic programming, autoepistemic logic, non-monotonic modal logics, and certain instances of circumscription as special cases, is presented and a more liberal, argumentation-theoretic semantics is proposed, based upon the notion of admissible extension in logic programming.

TL;DR: It is shown that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations, and a model-based representation theorem is established which characterizes the proposed postulates and constrains the way in which entrenchment orderings may be transformed under iterated belief revision.

TL;DR: It is argued that in order to address the challenge of building coordinated and collaborated intelligent agents, it is beneficial to combine AI techniques with methods and techniques from a range of multi-entity fields, such as game theory, operations research, physics and philosophy.

TL;DR: A normative, application- and protocol-independent theory of coalitions among bounded-rational agents is devised, and the optimal coalition structure and its stability are significantly affected by the agents' algorithms' performance profiles and the cost of computation.

TL;DR: A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data, and has been evaluated in several clinical domains and in an engineering domain, with encouraging results.

TL;DR: Interactive software and database search strategies that facilitate the discovery of previously unknown cross specialty information of scientific interest are described and evaluated.

TL;DR: It is argued that the ICL provides a natural and concise representation for multi-agent decision-making under uncertainty that allows for the representation of structured probability tables, the dynamic construction of networks and a way to handle uncertainty and decisions in a logical representation.

TL;DR: A gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice has been discussed in this article, where some directions for future research are indicated.

TL;DR: A theory of abstract argumentation systems is developed to find out which arguments eventually emerge undefeated, and it is established that arguments that are in force are precisely those in the limit of a so-called complete argumentation sequence.

TL;DR: The model presented in this paper has potential applications in areas as diverse as Geographical Information Systems (GIS), Computer Aided Design (CAD), and Document Recognition.

TL;DR: This work introduces a simple and natural strategy-selection rule, called highest cumulative reward (HCR), and shows a class of games in which HCR guarantees eventual convergence to a rationally acceptable social convention.

TL;DR: This short paper relates the conditional object-based and possibility theory-based approaches for reasoning with conditional statements pervaded with exceptions to other methods in nonmonotonic reasoning, showing Lehmann's preferential and rational closure entailments which obey normative postulates, the infinitesimal probability approach, and the conditional (modal) logics-based approach to be equivalent.

TL;DR: The Gala system is described, an implemented system that allows the specification and efficient solution of large imperfect information games and provides a new declarative language for compactly and naturally representing games by their rules.

TL;DR: This work argues for the necessity of an approach based on causality by elaborating the limitations of common paradigms employed to handle ramifications—the principle of categorization and the policy of minimal change.

TL;DR: A notion of inconsistency between instantiations and variables is introduced, and is shown to be a useful tool for characterizing such well-known concepts as backtrack, backjump, and domain annihilation.

TL;DR: This paper considers the general problem of progressing an initial database in response to a given sequence of actions and appeals to the situation calculus and an axiomatization of actions which addresses the frame problem.

TL;DR: An abstraction technique for MDPs that allows approximately optimal solutions to be computed quickly and described methods by which the abstract solution can be viewed as a set of default reactions that can be improved incrementally, and used as a heuristic for search-based planning or other MDP methods.

TL;DR: It is shown that learning from interpretations reduces toLearning from entailment, which in turn reduces to learning from satisfiability, which reduces to inductive logic programming and computational learning theory.

TL;DR: A new cognitive architecture for artificial vision aimed at an autonomous intelligent system is proposed, which concerns the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level: the exploration process of the perceived scene is driven by linguistic and associative expectations.

TL;DR: A new method for continuous case-based reasoning is introduced and its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system) is discussed.

TL;DR: It is proved that the bounded-variance algorithm is the first algorithm with provably fast inference approximation on all belief networks without extreme conditional probabilities, and it is shown that this algorithm approximates inference probabilities in worst-case time that is subexponential 2 (log n ) d , for some integer d that is a linear function of the depth of the belief network.

TL;DR: This paper describes a powerful extension of this approach for handling complex goals for reactive agents by using a modal temporal logic that can express quite complex time, safety, and liveness constraints.

TL;DR: Semiquantitative simulation using intervals in an implemented semi-quantitative simulator called Q3 is demonstrated, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques.

TL;DR: A high-level language for describing actions, more expressive than the action language A introduced by Gelfond and Lifschitz, that allows actions with indirect effects, nondeterministic actions, and actions that may be impossible to execute to be described.

TL;DR: A novel method for closed-world inference and update over the first-order theories of action used by planning algorithms such as nonlin, tweak and ucpop is described and it is demonstrated that incompleteness is nonproblematic in practice.