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Abductive Reasoning

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Hidenori Itoh – One of the best experts on this subject based on the ideXlab platform.

  • a parallel cost based Abductive Reasoning system on workstation cluster
    Systems and Computers in Japan, 2006
    Co-Authors: Shohei Kato, Tomonori Nakamura, Hidenori Itoh

    Abstract:

    This paper proposes a method for dynamic load balancing which works efficiently in the workstation cluster environment. A parallel Abductive Reasoning system based on the proposed method is also proposed. In the workstation cluster environment, parallel processing is performed using workstations with various processing abilities and loads. Thus, dynamic load balancing must consider the performance and loading conditions of the workstations. This paper proposes a method of estimating the processing ability of the workstation during operation, and also a dynamic load balancing algorithm to balance the estimated conditions. Using the proposed method, a parallel Abductive Reasoning system which can derive the most preferable explanation for a given observation is implemented. The results of experiments on the system are also reported. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(3): 80–89, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.10190

  • PRICAI – Parallel cost-based Abductive Reasoning for distributed memory systems
    Lecture Notes in Computer Science, 1996
    Co-Authors: Shohei Kato, Hirohisa Seki, Hidenori Itoh

    Abstract:

    This paper describes efficient parallel first-order cost-based Abductive Reasoning for distributed memory systems. A search control technique of parallel best-first search is introduced into Abductive Reasoning mechanism, thereby finding much more efficiently a minimal-cost explanation of a given observation. We propose a PARallel Cost-based Abductive Reasoning system, PARCAR, and give an informal analysis of PARCAR. We also implement PARCAR on an MIMD distributed memory parallel computer, Fujitsu AP1000, and show some performance results.

Shohei Kato – One of the best experts on this subject based on the ideXlab platform.

  • a parallel cost based Abductive Reasoning system on workstation cluster
    Systems and Computers in Japan, 2006
    Co-Authors: Shohei Kato, Tomonori Nakamura, Hidenori Itoh

    Abstract:

    This paper proposes a method for dynamic load balancing which works efficiently in the workstation cluster environment. A parallel Abductive Reasoning system based on the proposed method is also proposed. In the workstation cluster environment, parallel processing is performed using workstations with various processing abilities and loads. Thus, dynamic load balancing must consider the performance and loading conditions of the workstations. This paper proposes a method of estimating the processing ability of the workstation during operation, and also a dynamic load balancing algorithm to balance the estimated conditions. Using the proposed method, a parallel Abductive Reasoning system which can derive the most preferable explanation for a given observation is implemented. The results of experiments on the system are also reported. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(3): 80–89, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.10190

  • PRICAI – Parallel cost-based Abductive Reasoning for distributed memory systems
    Lecture Notes in Computer Science, 1996
    Co-Authors: Shohei Kato, Hirohisa Seki, Hidenori Itoh

    Abstract:

    This paper describes efficient parallel first-order cost-based Abductive Reasoning for distributed memory systems. A search control technique of parallel best-first search is introduced into Abductive Reasoning mechanism, thereby finding much more efficiently a minimal-cost explanation of a given observation. We propose a PARallel Cost-based Abductive Reasoning system, PARCAR, and give an informal analysis of PARCAR. We also implement PARCAR on an MIMD distributed memory parallel computer, Fujitsu AP1000, and show some performance results.

P Venkataram – One of the best experts on this subject based on the ideXlab platform.

  • REALISTIC Abductive Reasoning-BASED FAULT AND PERFORMANCE MANAGEMENT IN COMMUNICATION NETWORKS
    Journal of the Indian Institute of Science, 2013
    Co-Authors: G. Prem Kumar, P Venkataram

    Abstract:

    Abductive Reasoning is identified as a suitable candidate for solving network fault and performance management problems. A method to solve the network fault diagnosis problem using realistic Abductive Reasoning model is proposed. The realistic Abductive inference mechanism is based on the parsimonious covering theory with some new features added to the Abductive Reasoning model. The network diagnostic knowledge is assumed to be represented in the most general form of causal chaining, namely, hyper- bipartite network. As many explanations may still be generated by the realistic Abductive Reasoning model, we propose a probabilistic method to order them so as to try out the diagnostic explanation in the decreasing order of plausibility until the hard failure-like faulty device is isolated and replaced/cop-ected. In contrast, performance degradation in communication networks can be viewed to be caused by a set of faults, called soft failures. owing to whkh the network resources like bandwidth cannot be utilized to the expected level. An automated solution to the perfonnance management problem involves identifying these soft failures and use/suggest suitable remedies to tune the network for better performance. Abductive reas.oning model is used again to identify the network soft failures and suggest remedies. Common channel signalling network fault  management and Ethernet performance management are taken up as case studies. The results obtained by the proposed approach are encouraging.

  • Probabilistic Extension to Realistic Abductive Reasoning Model
    IETE Journal of Research, 1996
    Co-Authors: Gp Kumar, P Venkataram

    Abstract:

    In this paper, we give a method for probabilistic assignment to the Realistic Abductive Reasoning Model, The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning probability to the explanations, First, the inference mechanism using realistic Abductive Reasoning model is briefly described and then probability is assigned to each of the explanations so as to pick up the explanations in the decreasing order of plausibility.

  • Integrated Network Management – Network performance management using realistic Abductive Reasoning model
    Integrated Network Management IV, 1995
    Co-Authors: G. Prem Kumar, P Venkataram

    Abstract:

    Performance degradation in communication networks can be viewed to be caused by a set of faults, called soft failures, owing to which the network resources like bandwidth can not be utilized to the expected level. An automated solution to the performance management problem involves identifying these soft failures and use/suggest suitable remedies to tune the network for better performance. Abductive Reasoning model is identified as a suitable candidate for the network performance management problem. An approach to solve this problem using the realistic Abductive Reasoning model is proposed. The realistic Abductive inference mechanism is based on the parsimonious covering theory with some new features added to the general Abductive Reasoning model. The network performance management knowledge is assumed to be represented in the most general form of causal chaining, namely, hyper-bipartite network. Ethernet performance management is taken up as a case study. The results obtained by the proposed approach demonstrate its effectiveness in solving the network performance management problem.