317 research outputs found
An algorithm for the induction Of defeasible logic theories from databases
Defeasible logic is a non-monotonic logic with applications in rule-based domains such as law. To ease the development and improve the accuracy of expert systems based on defeasible logic, it is desirable to automatically induce a theory of the logic from a training set of precedent data. Empirical evidence suggests that minimal theories that describe the training set tend to be more faithful representations of reality. We show via transformation from the hitting set problem that this global minimization problem is intractable, belonging to the class of NP optimisation problems. Given the inherent difficulty of finding the optimal solution, we instead use heuristics and demonstrate that a best-first, greedy, branch and bound algorithm can be used to find good theories in short time. This approach displays significant improvements in both accuracy and theory size as compared to recent work in the area that post-processed the output of an Aprori association rule-mining algorithm, with comparable execution times
Induction of defeasible logic theories in the legal domain
The market for intelligent legal information systems remains relatively untapped and while this might be interpreted as an indication that it is simply impossible to produce a system that satisfies the needs of the legal community, an analysis of previous attempts at producing such systems reveals a common set of deficiencies that in-part explain why there have been no overwhelming successes to date. Defeasible logic, a logic with proven successes at representing legal knowledge, seems to overcome many of these deficiencies and is a promising approach to representing legal knowledge. Unfortunately, an immediate application of technology to the challenges in this domain is an expensive and computationally intractable problem. So, in light of the benefits, we seek to find a practical algorithm that uses heuristics to discover an approximate solution. As an outcome of this work, we have developed an algorithm that integrates defeasible logic into a decision support system by automatically deriving its knowledge from databases of precedents. Experiments with the new algorithm are very promising - delivering results comparable to and exceeding other approaches
Representation results for defeasible logic
The importance of transformations and normal forms in logic programming, and
generally in computer science, is well documented. This paper investigates
transformations and normal forms in the context of Defeasible Logic, a simple
but efficient formalism for nonmonotonic reasoning based on rules and
priorities. The transformations described in this paper have two main benefits:
on one hand they can be used as a theoretical tool that leads to a deeper
understanding of the formalism, and on the other hand they have been used in
the development of an efficient implementation of defeasible logic.Comment: 30 pages, 1 figur
Embedding Defeasible Logic into Logic Programming
Defeasible reasoning is a simple but efficient approach to nonmonotonic
reasoning that has recently attracted considerable interest and that has found
various applications. Defeasible logic and its variants are an important family
of defeasible reasoning methods. So far no relationship has been established
between defeasible logic and mainstream nonmonotonic reasoning approaches.
In this paper we establish close links to known semantics of logic programs.
In particular, we give a translation of a defeasible theory D into a
meta-program P(D). We show that under a condition of decisiveness, the
defeasible consequences of D correspond exactly to the sceptical conclusions of
P(D) under the stable model semantics. Without decisiveness, the result holds
only in one direction (all defeasible consequences of D are included in all
stable models of P(D)). If we wish a complete embedding for the general case,
we need to use the Kunen semantics of P(D), instead.Comment: To appear in Theory and Practice of Logic Programmin
A Potpourri of Reason Maintenance Methods
We present novel methods to compute changes to materialized
views in logic databases like those used by rule-based reasoners.
Such reasoners have to address the problem of changing axioms in the
presence of materializations of derived atoms. Existing approaches have
drawbacks: some require to generate and evaluate large transformed programs
that are in Datalog - while the source program is in Datalog and
significantly smaller; some recompute the whole extension of a predicate
even if only a small part of this extension is affected by the change.
The methods presented in this article overcome these drawbacks and derive
additional information useful also for explanation, at the price of an
adaptation of the semi-naive forward chaining
Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web
In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers
Combining argumentation and clustering techniques in pattern classification problems
Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one.
In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI
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