Forward chaining

Abstract: An algorithm based on fuzzy set logic and nonlinear program- ming optimization is proposed for aggregating antecedents within a template or rule into a single valued entity for use in a detachment or implication operator for forward chaining in an expert system. The method assumes that confidences of the observed antecedents can be sorted, otherwise a paired comparison weighting developed by Saaty [1] must be employed to equalize the relative importances of the arguments before sorting is performed. The method is based on a new type of fuzzy Ordered Weighted Average (OWA) operator proposed by Yager [2,3]. An offline nonlinear program (geometric program) involving only two optimization variables is used to develop the weights using a formulation by O'Hagan [4]. This formulation is equivalent to the well-known Gibbs free energy problem of chemical engineering [5,6]. The proposed aggregation method is computationally efficient involving only an 0(n - In(n)) sort and an 0(n) inner product when aggregating n antecedents and is ideally suited for real-time expert system or fuzzy multi- objective decision applications.

Abstract: The paper presents an approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low-level image processing modules and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatio-temporal analysis during daytime, and by morphological analysis of headlights at night. The high-level module is designed as a forward chaining production rule system, working on symbolic data, i.e., vehicles and their attributes (area, pattern, direction, and others) and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness.

Abstract: We examine methods of implementing queries about relational databases in the case where these queries are expressed in first-order logic as a collection of Horn clauses. Because queries may be defined recursively, straightforward methods of query evaluation do not always work, and a variety of strategies have been proposed to handle subsets of recursive queries. We express such query evaluation techniques as “capture rules” on a graph representing clauses and predicates. One essential property of capture rules is that they can be applied independently, thus providing a clean interface for query-evaluation systems that use several different strategies in different situations. Another is that there be an efficient test for the applicability of a given rule. We define basic capture rules corresponding to application of operators from relational algebra, a top-down capture rule corresponding to “backward chaining,” that is, repeated resolution of goals, a bottom-up rule, corresponding to “forward chaining,” where we attempt to deduce all true facts in a given class, and a “sideways” rule that allows us to pass results from one goal to another.