Above are papers related to the following paper: [Paper_ID] Paper #683360. 	[Title] Some Varieties of Qualitative Probability [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132864. 	[Title] Inference in Cognitive Maps [Abstract] Cognitive mapping is a qualitative decision modeling technique developed over twenty years ago by political scientists, which continues to see occasional use in social science and decision-aiding applications. In this paper, I show how cognitive maps can be viewed in the context of more recent formalisms for qualitative decision modeling, and how the latter provide a firm semantic foundation that can facilitate the development of more powerful inference procedures as well as extensions in expressiveness for models of this sort. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #522338. 	[Title] Explaining "Explaining Away" [Abstract] Explaining away is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also can occur, in which the confirmation of one cause increases belief in another. We provide a general qualitative probabilistic analysis of intercausal reasoning, and identify the property of the interaction among the causes, product synergy, that determines which form of reasoning is appropriate. Product synergy extends the qualitative probabilistic network (QPN) formalism to support qualitative intercausal inference about the directions of change in probabilistic belief. The intercausal relation also justifies Occam's razor, facilitating pruning in search for likely diagnoses. 0 Portions of this paper originally appeared in Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning [16]. y Supported by the National Science Foundation under grant IRI-8807061 to Carnegie Mellon and by the Rockwell International Science Center. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189856. 	[Title] State-Space Abstraction for Anytime Evaluation of Probabilistic Networks [Abstract] One important factor determining the computa - tional complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an any - time procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the proce - dure exhibits a smooth improvement in approxi - mation quality as computation time increases. This suggests that statespace abstraction is one more useful control parameter for designing real-time probabilistic reasoners. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #683404. 	[Title] Incremental Tradeoff Resolution in Qualitative Probabilistic Networks [Abstract] Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108983. 	[Title] Exploiting Causal Independence in Bayesian Network Inference [Abstract] A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as or, sum or max, on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #683355. 	[Title] Using Qualitative Relationships for Bounding Probability Distributions [Abstract] We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that are guaranteed to bound the distributions of interest in the desired direction. By evaluating incrementally improved approximate networks, our algorithm obtains monotonically tightening bounds that converge to exact distributions. For supermodular utility functions, the tightening bounds monotonically reduce the set of admissible decision alternatives as well. Please summarise all the given information.

Paper #683360 has the title `Some Varieties of Qualitative Probability  
` and the abstract ` 
`.
Question: Which category should Paper #683360 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #683360 has the title `Some Varieties of Qualitative Probability  
` and the abstract ` 
`.
Question: Which category should Paper #683360 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods