Abstract-Datalog is a database query language based on the logic programming paradigm; it has been designed and intensively studied over the last five years. We present the syntax and semantics of Datalog and its use for querying a relational database. Then, we classify optimization methods for achieving efficient evaluations of Datalog queries, and present the most relevant methods. Finally, we discuss various exhancements of Datalog, currently under study, and indicate what is still needed in order to extend Datalog’s applicability to the solution of real-life problems. The aim of this paper is to provide a survey of research performed on Datalog, also addressed to those members of the database community who are not too familiar with logic programming concepts. Zndex Terms-Deductive databases, logic programming, recursive queries, relational databases, query optimization. I.

W’e present a formal s \ ntax and semantics for SNePS considered as the (modeled) mind of a cogn:ti\e agent. The semantics is based on a Meinongian theory of ’ the intensional objects of ’ thought that is appropriate for 41 considered as “computational philosophy ” or “computational psychology”. 1. INTRODUCTION. W’e present a formal syntax and semantics for the SNePS Semantic Network P recessing System (Shapiro 1979), based on a \leinongian theory of the intensional objects of thought (Rapaport 198Sa). Such a theory avoids possible worlds and is appropriate

It is generally recognized that the possibdity of detecting contradictions and identifying their sources is an important feature of an intelligent system. Systems that are able to detect contradictions, identify their causes, or readjust their knowledge bases to remove the contradiction, called Belief Revision Systems. Truth Maintenance Systems, or Reason Maintenance Systems. have been studied by several researchers in Artificial bttelligence ( AI). In this paper, we present a logic suitable for supporting belief revision systems, discuss the properties that a belief revision system based on this logic will exhibit, and present a particular intplementation of our model of a belief revision system. The system we present differs from most of the systems developed so far in three respects: First, it is baseti on a logic that was developed to support belief revision systems. Second, it uses the rules of inference of the logic to automatically compute the dependencies among propositions rather than having to force the user to do titis, as in many existing systems. Third, it was the first belief revision system whose implementation relies on the manipulation of sets of assumptions, not justifications.

this article.

The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. This chapter presents several aspects of such a logic.

This paper treats the problem of implementing efficiently recursive Horn Clauses queries, including those with function symbols. In particular, the situation is studied where the initial bindings of the arguments in the recursire query goal can be used in the top-down (as in backward chaining) execution phase to improve the efficiency and, often, to guarantee the termination, of the forward chaining execution phase that implements the fixpoint computation for the recursire query. A general method is given for solving these queries; the method performs an analysis of the binding passing behavior of the query, and then reschedules the overall execution as two fixpoint computations derived as results of this analysis. The first such computation emulates the propagation of bindings in the top-down phase; the second generates the desired answer by proving the goals left unsolved during the previous step. Finally, sufficient conditions for safety are derived, to ensure that the fixpoint computations are completed in a finite number of steps.

We describe a method for query evaluation in deductive databases which is based on dynamic filtering of data flow. The basic query evaluation strategy is bottom-up and set-oriented. The method imposes no restrictions on the form of Horn axioms, takes advantage of actually stored data, allows compile time preprocessing, and is well suited for parallel and distributed execution. 1

Much recent work in reasoning systems has concentrated on the role of time in planning, action modeling, and tasks in domains where time is important. On the other hand, there are systems that concentrate on spatial reasoning, especially where manipulation or managing of the environment is important, as in robot route planning. The integration of the two themes is a goal which, if possible, would allow the interaction of space and time to be explored. A problem-solving system would then be able to reason about the times at which actions might occur, in the light of spatial constraints, or vice versa, to reason about the places in which actions take place, and the temporal constraints involved. This paper showsaway tointegrate the representation of both time and space in a framework that allows uniform reasoning across both dimensions. An ontology for objects, events, states, processes is provided using conceptual graphs for representation, and a syntactic extension to Sowa's conceptual graph formalism (see Sowa's article, this volume) is presented to support the e ort. 1

General knowledge is widely applicable, but relatively slow to apply to any particular situation. Specific knowledge can be used rapidly where it applies, but is only narrowly applicable. We present an automatic scheme to migrate general knowledge to specific knowledge during reasoning. This scheme relies on a nested rule representation which retains the rule builder's intentions about which of the possible specializations of the rule will be most useful. If both general and specific knowledge is available and applicable, a system maybeslowed down by trying to use the general knowledge as well as, or instead of, the specific knowledge. However, if general knowledge is purged from the system after migration, the system will lose the flexibility of being able to handle different situations. To retain the flexibility without paying the price in speed, a shadowing scheme is presented that prevents general knowledge from being used when specific knowledge migrated from it is available and applicable. The combination of knowledge migration and knowledge shadowing allows a deductive reasoning system to learn from and exploit previous experience. Experience is represented by the instance relationship between the general knowledge and the specific knowledge migrated from it. We also

SNePS, the Semantic Network Processing System, has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). This paper expands on this motivation, discusses some of the system features that derived from this motivation, and presents four case studies of interactions with SNePS demonstrating some of these features. The features demonstrated in the case studies are: nonstandard connectives; the use of recursive rules; the Unique Variable Binding Rule, that says that two variables in a rule cannot be instantiated to the same term; and discussing sentences and propositions in natural language. 1 System Description SNePS, the Semantic Network Processing System [9, 15, 17], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). It has always been the intention that a SNePSbased "knowledge base" would ultimately be built, not by a programmer or k...