Explaining “Explaining Away”
"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 an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is provided and the property of the interaction among the causes (product synergy) that determines which form of reasoning is appropriate is identified. 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 the search for likely diagnoses.
A market-oriented programming environment and its application to distributed multicommodity flow problems
Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.
The Automated Mapping of Plans for Plan Recognition
To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.
State-space Abstraction for Anytime Evaluation of Probabilistic Networks
One important factor determining the computational 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 anytime procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.
Inference in cognitive maps
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.
