| F. Voorbraak. Combining evidence under partial ignorance. In Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning, pages 574-588, 1997. |
....4 Evidential reasoning Once the various veri cation modules have generated scores for each of the candidate ordnance locations, we must have some method for combining the scores into a single measure that can be used to evaluate each candidate. We use a linear opinion pool (see, for example, [3]) where the results of each measurement are combined according to some weighting factor that represents the con dence in the probability estimate that is generated. Let H be the hypothesis that a certain candidate actually represents an ordnance instance. Each veri cation module v yields a ....
F. Voorbraak. Combining evidence under partial ignorance. In Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning, pages 574-588, 1997.
....2.5 Evidential reasoning Once the various verification modules have generated scores for each of the candidate ordnance locations, we must have some method for combining the scores into a single measure that can be used to evaluate each candidate. We use a linear opinion pool (see, for example, [11]) where the results of each measurement are combined according to a weighting factor that is determined along with the probability result. The weighting factor represents the confidence in the probability estimate that is generated. Let H be the hypothesis that a certain candidate actually ....
F. Voorbraak. Combining evidence under partial ignorance. In Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning, pages 574--588, 1997.
....for choice functions, generalised choice independence and choice independence are equivalent. In section 7, we will see that a satisficing procedure can satisfy generalised choice independence, whereas the underlying preference ordering does not satisfy order independence. 5 Partial Ignorance In [17, 18] we introduce a generalization of Bayesian probability theory called partial probability theory (PPT) In PPT, a belief state is represented by constraints on probability measures representing generic information about the situation plus a set C representing the available specific evidence on ....
F. Voorbraak, Combining evidence under partial ignorance. Qualitative and Quantitative Practical Reasoning. Proc. ECSQARU-FAPR'97 , D. Gabbay et al. (eds.), Springer, Berlin (1997) 574-588.
....process of building a map from sonar data. Other examples of application areas where it is very hard to obtain the necessary (exact) probabilities are sensor fusion and dynamic environments. In sensor fusion the problem is that the exact interaction of different sensors is difficult to assess (see [16,18,19]) In a dynamic environment it is very hard to justify a particular rate at which the confidence in conclusions based on former observations should decrease as time passes (see [3] Throughout this paper we will discuss a very simple example where a robot has to choose between two (or more) ....
....we argue that, especially in contexts where it is important to know whether or not to obtain additional information, decision analysis should not be exclusively focused on optimizing but pay more serious attention to satisficing and reasoning with assumptions. 2. Partial probability theory In [16,17,18,19] we introduce a generalization of Bayesian probability theory called partial probability theory (PPT) In PPT, a belief state is represented by constraints on probability measures representing generic information about the situation plus a set C representing the available specific evidence on ....
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F. Voorbraak. Combining evidence under partial ignorance. Qualitative and Quantitative Practial Reasoning. Proc. ECSQARU-FAPR'97, D. Gabbay et al. (eds.),, Springer, Berlin (1997) 574-588.
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