This article argues for, and describes some of the advantages of, construing level one information fusion, as a task of abductive inference or inference to the best explanation. Such an approach enables certain benefits, such as, an expectation-based critique of hypotheses, and an elegant system for revising old beliefs, which may gainfully be exploited. It also introduces several relevant dimensions to reasoning based on the explanatory relations between hypotheses and data, that are closed to traditional approaches. The design principles of a software system, Smart-ASAS, that attempts to solve the level one fusion task of entity tracking and re-identification, are described, along with an example that illustrates its capabilities

Abductive inferences seem to be ubiquitous in cognition, and cognitive agents often solve complex abduction tasks very rapidly. However, abduction characterized as ‘inference to the best explanation’ is in general computationally intractable. This paper describes three related ideas for understanding how intelligent agents might efficiently perform abduction tasks. First, we recharacterize the abduction task as inference to a confident explanation, where a confident explanation is internally consistent, parsimonious, distinctly more plausible than alternative explanations, and explains as much of the data as possible with high confidence. Second, we describe a decomposition of the task of synthesizing a confident explanation into several subtasks so that the synthesis starts from islands of relative certainty and then grows opportunistically. This decomposition helps in controlling the computational cost of accommodating interactions among explanatory hypotheses, especially incompatibility interactions. Third, we present a concurrent mechanism for synthesizing confident explanations. The mechanism exploits data and processing dependencies afforded by the decomposition of the synthesis task. The emphasis of this approach to abduction is on characterizing the constraints of the abduction task and exploiting these constraints for making abductive inferences. In describing this approach, we also clarify the precise class of abduction problems addressed by the RED-2 system, and report on some new experiments. The main result is a computational model that not only enables efficient abductive inferences but also accommodates explanatory interactions, uncertainty, and data collection.

Abduction, or inference to the best explanation, has been used in artificial intelligence as a framework for solving problems ranging from diagnosis, to test interpretation, to theory formation, to natural language understanding, to perception. Previous research on computational models of abduction has suggested that a single generic strategy may be used to perform abductions in a variety of domains and across very diverse problems. This paper describes a generic strategy for abduction and a tool, Peirce, for constructing abductive problem solving systems.>

A general problem-solving mechanism is described that is especially suited for performing a particular form of abductive inference, or best explanation finding. A problem solver embodying this mechanism synthesizes composite hypotheses by combining simple hypotheses to satisfy explanatory goals. These simple hypotheses are formed by instantiating prestored explanatory "concepts." In this way the problem solver is able to arrive at complex integrated conclusions which are not prestored. A computationally feasible task-specific problem-solving mechanism is presented for a particular information-processing task which is, nevertheless, of very great generality. The task is that of synthesizing coherent composite explanatory hypotheses based upon a prestored and possibly vast collection of hypothesis-generating concepts. This is seemingly a common task of intelligence and potentially a major component of diagnostic reasoning, especially where single-fault assumptions are inappropriate. The mechanism is described both functionally and structurally; that is, the why and what of the main computations are described, together with algorithms that show how each of these computations can be accomplished. The mechanism integrates a classification machine, used for selecting plausible hypotheses, with a specialized means-ends machine, used for assembling a best explanation from the plausible hypotheses thus selected and for pointedly investigating alternative explanations. There are also two other specialized mechanisms for the subsidiary functions of recognizing the applicability of a hypothesis to the situation and of interpreting the situation-specific raw data to satisfy the informational needs of the other components.