Conference Paper

An Evolutionary Optimization Approach to Cost-Based Abduction, with Comparison to PSO

Univ. of North Carolina in Washington, Washington
DOI: 10.1109/IJCNN.2007.4371425 Conference: Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Source: IEEE Xplore

ABSTRACT

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we apply an evolutionary algorithm (EA) to the problem of finding least-cost proofs in cost-based abduction systems, comparing performance to PSO using a difficult problem instance.

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    • "Chivers et al. showed promise in the use of genetic algorithms for finding approximate solutions [15]. In our experiments, we directly contrast our results with [12], [13], [14] and [15] which use the same CBA problem set. Our approach is to use a membrane architecture to exploit the inherit parallelism of the architecture combined with the genetic recombination of strings. "
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    ABSTRACT: This paper describes the parallelization of a membrane computing architecture in solving cost-based abduction (CBA) optimization problems. Membrane systems are a class of distributed, massively parallel and non-deterministic data structures based on the biological metaphor of cells and cell processes. As such, algorithms based on these cell processes are suitable for implementation on parallel machines, and because of the localized nature of the communication between cells, load balancing is easier to predict and dynamically evaluate. Cost-based abduction is an important problem in reasoning in uncertainty with applications in medical diagnostics, natural language processing, belief revision, and automated planning. In this paper, we will describe a membrane architecture used to search for optimal solutions to cost-based abduction problems, compare the performance of this algorithm to other published techniques and present empirical results on the parallelization in a cluster computing environment.
    Full-text · Conference Paper · Jan 2008
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    • "Chivers et al. showed promise in the use of genetic algorithms for finding approximate solutions [15]. In our experiments, we directly contrast our results with [12], [13], [14] and [15] which use the same CBA problem set. "
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    ABSTRACT: This paper describes the first application of membrane computing to the cost-based abduction (CBA) optimization problem. Membrane systems are a class of distributed, massively parallel and non-deterministic data structures based on the biological metaphor of cells and cell processes. As such, algorithms based on these cell processes are suitable for implementation on massively parallel machines, and because of the localized nature of the communication between cells, load balancing is easier to predict and dynamically evaluate. Cost-based abduction is an important problem in reasoning in uncertainty with applications in medical diagnostics, natural language processing, belief revision, and automated planning. In this paper, we will describe a membrane architecture used to search for optimal solutions to cost-based abduction problems, compare the performance of this algorithm to other published techniques and present empirical results on the efficacy of various topologies of membrane structures.
    Full-text · Conference Paper · Nov 2007
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    ABSTRACT: Abduction is inference to the best explanation. Abduction has long been studied intensively in a wide range of contexts, from artificial intelligence research to cognitive science. While recent advances in large-scale knowledge acquisition warrant applying abduction with large knowledge bases to real-life problems, as of yet no existing approach to abduction has achieved both the efficiency and formal expressiveness necessary to be a practical solution for large-scale reasoning on real-life problems. The contributions of our work are the following: (i) we reformulate abduction as an Integer Linear Programming (ILP) optimization problem, providing full support for first-order predicate logic (FOPL); (ii) we employ Cutting Plane Inference, which is an iterative optimization strategy developed in Operations Research for making abductive reasoning in full-fledged FOPL tractable, showing its efficiency on a real-life dataset; (iii) the abductive inference engine presented in this paper is made publicly available.
    Full-text · Conference Paper · Sep 2012