3,829 research outputs found
AN APPROACH TO DEPENDENCY DIRECTED BACKTRACKING USING DOMAIN SPECIFIC KNOWLEDGE
The idea of dependency directed backtracking proposed by Stallman and Sussman (1977)
offers significant advantages over heuristic starch schemes with chronological
backtracking which waste much effort by discarding many "good" choices when
backtracking situations arise. However, we have found that existing non-chronological
backtracking machinery is not suitable for certain types of problems, namely, those
where choices do not follow logically from previous choices, but are based on a heuristic
evaluation of a constrained set of alternatives. This is because a choice is not justified by
a âset of supportâ (of previous choices), but because its advantages outweigh its
drawbacks in comparison to its competitors. What is needed for these types of problems
is a scheme where the advantages and disadvantages of choices are explicitly recorded
during problem solving. Then, if an unacceptable situation arises, information about the
nature of the unacceptability and the tradeoffs can be used to determine the most
appropriate backtracking point. Further, this requires the problem solver to use its
hindsight to preserve those "good" intervening choices that were made chronologically
after the "bad" choice, and to resume its subsequent reasoning in fight of the modified
set of constraints. In this paper, we describe a problem solver for non-chronological
backtracking in situations involving tradeoffs. By endowing the backtracker with access
to domain-specific knowledge, a highly contextual approach to reasoning in dependency
directed backtracking situations can be achieved.Information Systems Working Papers Serie
Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation
We argue for a performance-based design of natural language grammars and
their associated parsers in order to meet the constraints imposed by real-world
NLP. Our approach incorporates declarative and procedural knowledge about
language and language use within an object-oriented specification framework. We
discuss several message-passing protocols for parsing and provide reasons for
sacrificing completeness of the parse in favor of efficiency based on a
preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st
Reason Maintenance - State of the Art
This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques
Knowledge-Based Schematics Drafting: Aesthetic Configuration as a Design Task
Depicting an electrical circuit by a schematic is a tedious task that is a good candidate for automation. Programs that draft schematics with the usual algorithmic approach do not fully exploit knowledge of circuit function, relying mainly on the circuit topology. The extra-topological circuit characteristics are what an engineer uses to understand a schematic; human drafters take these characteristics into account when drawing a schematic.
This document presents a knowledge base and an architecture for drafting arithmetic digital circuits having a single theme. The relevance and limitations of this architecture and knowledge base for other types of circuit are explored.
It is argued that the task of schematics drafting is one of aesthetic design. The affect of aesthetic criteria on the program architecture is discussed. The circuit layout constraint language, the program's search regimen, and the backtracking scheme are highlighted and explained in detail.MIT Artificial Intelligence Laborator
An Architectural Approach to Ensuring Consistency in Hierarchical Execution
Hierarchical task decomposition is a method used in many agent systems to
organize agent knowledge. This work shows how the combination of a hierarchy
and persistent assertions of knowledge can lead to difficulty in maintaining
logical consistency in asserted knowledge. We explore the problematic
consequences of persistent assumptions in the reasoning process and introduce
novel potential solutions. Having implemented one of the possible solutions,
Dynamic Hierarchical Justification, its effectiveness is demonstrated with an
empirical analysis
Decision-theoretic control of EUVE telescope scheduling
This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems
DEPENDENCY DIRECTED BACKTRACKING IN GENERALIZED SATISFICING ASSIGNMENT PROBLEMS
Many authors have described search techniques for the satisficing assignment problem: the problem of
finding an interpretation for a set of discrete variables that satisfies a given set of constraints. In this paper
we present a formal specification of dependency directed backtracking as applied to this problem. We
also generalize the satisficing assignment problem to include limited resource constraints that arise in
operations research and industrial engineering. We discuss several new search heuristics that can be
applied to this generalized problem, and give some empirical results on the performance of these
heuristics.Information Systems Working Papers Serie
The Use of Dependency Relationships in the Control of Reasoning
Research reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under contract N00014-75-C-0643.Several recent problem-solving programs have indicated improved methods for controlling program actions. Some of these methods operate by analyzing the time-independent antecedent-consequent dependency relationships between the components of knowledge about the problem for solution. This paper is a revised version of a thesis proposal which indicates how a general system of automatically maintained dependency relationships can be used to effect many forms of control on reasoning in an antecedent reasoning framework.MIT Artificial Intelligence Laboratory
Department of Defense Advanced Research Projects Agenc
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