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.

  • Network Fault Diagnosis Using a Realistic Abductive Reasoning Model
    Engineering Applications of Artificial Intelligence, 1995
    Co-Authors: G. Prem Kumar, P Venkataram
    Abstract:

    Abstract This paper proposes a method to solve the network fault diagnosis problem using the Realistic Abductive Reasoning Model. This model uses an Abductive inference mechanism based on the parsimonious covering theory, and adds some new features to the general model of diagnostic problem-solving. The network fault-diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, a hyper-bipartite graph. A layered graph is constructed from the given hyper-bipartite graph by the addition of a few dummy nodes. Then the diagnostic problem is solved, starting from the lowest layer of the layered graph, as a series of bipartite graphs, until the top-most layer is reached. The inference mechanism uses a Realistic Abductive Reasoning Model to diagnose the faults in a communication network, which is symptom-driven, based on some application programs. The hypothesis-test paradigm is used to refine the solution space. The fault-diagnostic capability of the proposed inference model is demonstrated by considering one node of a given network where the management information would be used to diagnose its local problems and the connectivity of the node in the network. The results obtained by the proposed model substantiate its effectiveness in solving network fault-diagnostic problems.

  • Fault diagnosis of telecommunication networks: Realistic Abductive Reasoning approach
    1995
    Co-Authors: Prem G Kumar, P Venkataram
    Abstract:

    In this paper, we propose a method to diagnose faults in telecommunication networks by using the realistic Abductive Reasoning model (Prem and Venkataram, 1994). This model uses the Abductive inference mechanism based on the parsimonious covering theory, with some new features added to the general model of diagnostic problem solving. The fault diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, a hyper-bipartite network. The results obtained by the proposed model demonstrate its effectiveness in solving telecommunication network fault diagnostic problems

Yun-bok Park - One of the best experts on this subject based on the ideXlab platform.

  • Roles of Abductive Reasoning and Prior Belief in Children's Generation of Hypotheses about Pendulum Motion
    Science & Education, 2006
    Co-Authors: Yong-ju Kwon, Jin-su Jeong, Yun-bok Park
    Abstract:

    The purpose of the present study was to test the hypothesis that student’s Abductive Reasoning skills play an important role in the generation of hypotheses on pendulum motion tasks. To test the hypothesis, a hypothesis-generating test on pendulum motion, and a prior-belief test about pendulum motion were developed and administered to a sample of 5th grade children. A significant number of subjects who have prior belief about the length to alter pendulum motion failed to apply their prior belief to generate a hypothesis on a swing task. These results suggest that students’ failure in hypothesis generation was related to Abductive Reasoning ability, rather than simple lack of prior belief. This study, then, supports the notion that Abductive Reasoning ability beyond prior belief plays an important role in the process of hypothesis generation. This study suggests that science education should provide teaching about Abductive Reasoning as well as scientific declarative knowledge for developing children’s hypothesis-generation skills.

  • Children's Generating Hypotheses on the Pendulum Motion: Roles of Abductive Reasoning and Prior Knowledge
    Journal of The Korean Earth Science Society, 2003
    Co-Authors: Jin-su Joeng, Yun-bok Park, Il-ho Yang, Yong-ju Kwon
    Abstract:

    The purpose of the present study was to test the hypothesis that student's Abductive Reasoning skills play an important role in the generation of hypotheses on pendulum motion tasks. To test the hypothesis, a hypothesis-generating test on the pendulum motion and a prior knowledge test about the length of the pendulum motion were developed and administered to a sample of 5th grade children. A significant number of subjects who have the prior knowledge about the length of the pendulum motion failed to apply that prior knowledge to generate a hypothesis on a swing task. These results showed that students' failure in hypothesis-generating was related to their deficiency in Abductive Reasoning ability, rather than the simple lack of prior knowledge. Furthermore, children's successful generating hypothesis should be required their Abductive Reasoning skills as well as prior knowledge. Therefore, this study supports the notion that Abductive Reasoning ability beyond prior knowledge plays an important role in the process of hypothesis-generation. This study suggests that science education should provide teaching about abdctive Reasoning as well as scientific declarative knowledge for developing children's hypothesis-generating skills.

Gerardo A Okhuysen - One of the best experts on this subject based on the ideXlab platform.