R. Alo

Publications (5)0 Total impact

• Source

  Available from: utep.edu

  Conference Proceeding: Optimization Techniques under Uncertain Criteria, and Their Possible Use in Computerized Education

  R. Alo, O. Kosheleva
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  ABSTRACT: The existing successful automated computerized systems more or less simulate the way successful human teachers teach. However, computerized systems provide more individualized options that traditional classroom education, and it is desirable to use this additional freedom to further improve the education success rate. In this papers, we briefly overview the experience of a successful Russian training system, and explain how general techniques of optimization under uncertainty can be used to optimize the content development
  Fuzzy Information Processing Society, 2006. NAFIPS 2006. Annual meeting of the North American; 07/2006
• Source

  Available from: psu.edu

  Conference Proceeding: Decision making for robust resilient systems

  R. Alo, A. de Korvin, F. Modave
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  ABSTRACT: Robust and resilient interconnected structures rely on decision procedures, both under uncertainty and multicriteria. In decision under uncertainty, we aim at finding a scoring procedure to determine an optimal decision without prior knowledge on the actual state of the world. In multicriteria decision making, the state of the world is known but we aim at ranking alternatives defined over a multidimensional set. Therefore, the problem is to find an appropriate aggregation procedure. In practical applications, we have to deal with decision problems where the state of the world is not known, and the alternatives are multidimensional. It is well known that the probability approach to these problem leads to paradoxes that are related to independence properties required on the preferences. This naturally leads us to drop additivity and therefore, replace probabilities with non-additive measures. The aim of this paper is to present several benefit functions to identify optimal actions, in the fuzzy measure and fuzzy logic perspective.
  System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on; 02/2003
• Source

  Available from: psu.edu

  Conference Proceeding: Applying fuzzy relation equations to threat analysis

  A. Berrached, M. Beheshti, A. de Korvin, R. Alo
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  ABSTRACT: Targeting behavior of vehicles in the battlefield (target analysis) is one of the most critical tasks in computer generated force (CGF) systems. This is simply because of many complex and ambiguous factors that can affect the targeting behavior of such systems in the real world. There have been many approaches including using fuzzy set theory for target analysis. Target detection, and threat analysis and selection are considered the main constituents of target analysis and selection. We introduce a model to illustrate how to apply a fuzzy relational equation algorithm to threat analysis in the context of computer generated force systems such as ModSAF (Modular Semi Automated Forces). Using fuzzy relational equations, the proposed algorithm generates data from the historic information and its earlier runs. Therefore, each new outcome of the algorithm is more realistic and more accurate than the earlier one.
  System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on; 02/2002
• Source

  Available from: psu.edu

  Conference Proceeding: Non-destructive testing of aerospace structures: granularity and data mining approach

  R. Osegueda, V. Kreinovich, L. Potluri, R. Alo
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  ABSTRACT: For large aerospace structures, it is extremely important to detect faults, and nondestructive testing is the only practical way to do it. Based on measurements of ultrasonic waves, Eddy currents, magnetic resonance, etc., we reconstruct the locations of the faults. The best (most efficient) known statistical methods for fault reconstruction are not perfect. We show that the use of expert knowledge-based granulation improves the quality of fault reconstruction
  Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on; 02/2002
• Source

  Available from: utep.edu

  Conference Proceeding: Fuzzy functions to select an optimal action in decision theory

  R. Alo, A. de Korvin, F. Modave
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  ABSTRACT: In decision under uncertainty, we aim at finding a scoring procedure to determine an optimal decision without prior knowledge on the actual state of the world. It is well known that the probability approach to this problem leads to paradoxes, that are related to independence properties required on the preferences. This naturally leads us to drop additivity and therefore, replace probabilities with fuzzy measures. The aim of this paper is to present several benefit functions to identify optimal actions, in the fuzzy measure and fuzzy logic perspective.
  Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American; 02/2002