58 research outputs found
Defeasible inheritance systems and reactive diagrams
We give an analysis of defeasible inheritance diagrams, also from the
perspective of reactive diagrams
Complexity of Prioritized Default Logics
In default reasoning, usually not all possible ways of resolving conflicts
between default rules are acceptable. Criteria expressing acceptable ways of
resolving the conflicts may be hardwired in the inference mechanism, for
example specificity in inheritance reasoning can be handled this way, or they
may be given abstractly as an ordering on the default rules. In this article we
investigate formalizations of the latter approach in Reiter's default logic.
Our goal is to analyze and compare the computational properties of three such
formalizations in terms of their computational complexity: the prioritized
default logics of Baader and Hollunder, and Brewka, and a prioritized default
logic that is based on lexicographic comparison. The analysis locates the
propositional variants of these logics on the second and third levels of the
polynomial hierarchy, and identifies the boundary between tractable and
intractable inference for restricted classes of prioritized default theories
Remarks on Inheritance Systems
We try a conceptual analysis of inheritance diagrams, first in abstract
terms, and then compare to "normality" and the "small/big sets" of preferential
and related reasoning. The main ideas are about nodes as truth values and
information sources, truth comparison by paths, accessibility or relevance of
information by paths, relative normality, and prototypical reasoning
Pseudo-contractions as Gentle Repairs
Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas
Default reasoning using maximum entropy and variable strength defaults
PhDThe thesis presents a computational model for reasoning with partial information
which uses default rules or information about what normally happens. The idea is
to provide a means of filling the gaps in an incomplete world view with the most
plausible assumptions while allowing for the retraction of conclusions should they
subsequently turn out to be incorrect. The model can be used both to reason from
a given knowledge base of default rules, and to aid in the construction of such
knowledge bases by allowing their designer to compare the consequences of his
design with his own default assumptions. The conclusions supported by the proposed
model are justified by the use of a probabilistic semantics for default rules
in conjunction with the application of a rational means of inference from incomplete
knowledge the principle of maximum entropy (ME). The thesis develops
both the theory and algorithms for the ME approach and argues that it should be
considered as a general theory of default reasoning.
The argument supporting the thesis has two main threads. Firstly, the ME approach
is tested on the benchmark examples required of nonmonotonic behaviour,
and it is found to handle them appropriately. Moreover, these patterns of commonsense
reasoning emerge as consequences of the chosen semantics rather than
being design features. It is argued that this makes the ME approach more objective,
and its conclusions more justifiable, than other default systems. Secondly, the
ME approach is compared with two existing systems: the lexicographic approach
(LEX) and system Z+. It is shown that the former can be equated with ME under
suitable conditions making it strictly less expressive, while the latter is too crude to
perform the subtle resolution of default conflict which the ME approach allows. Finally,
a program called DRS is described which implements all systems discussed
in the thesis and provides a tool for testing their behaviours.Engineering and Physical Science Research Council (EPSRC
Semantic networks
AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Logical tools for handling change in agent-based systems
We give a unified approach to various results and problems of nonclassical
logic
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