Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Improving PARMA Trailing

Taylor introduced a variable binding scheme for logic variables in his PARMA system, that uses cycles of bindings rather than the linear chains of bindings used in the standard WAM representation. Both the HAL and dProlog languages make use of the PARMA representation in their Herbrand constraint solvers. Unfortunately, PARMA's trailing scheme is considerably more expensive in both time and space consumption. The aim of this paper is to present several techniques that lower the cost. First, we introduce a trailing analysis for HAL using the classic PARMA trailing scheme that detects and eliminates unnecessary trailings. The analysis, whose accuracy comes from HAL's determinism and mode declarations, has been integrated in the HAL compiler and is shown to produce space improvements as well as speed improvements. Second, we explain how to modify the classic PARMA trailing scheme to halve its trailing cost. This technique is illustrated and evaluated both in the context of dProlog and HAL. Finally, we explain the modifications needed by the trailing analysis in order to be combined with our modified PARMA trailing scheme. Empirical evidence shows that the combination is more effective than any of the techniques when used in isolation. To appear in Theory and Practice of Logic Programming.Comment: 36 pages, 7 figures, 8 table

Offline Specialisation in Prolog Using a Hand-Written Compiler Generator

The so called "cogen approach" to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that the "cogen" approach is also applicable to the specialisation of logic programs (called partial deduction when applied to pure logic programs) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system LIX for pure logic programs. While this is a usable specialiser by itself, its specialisation strategy is used to develop the "cogen" system LOGEN. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, LOGEN generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the LOGEN system in a fully automated way Finally, LOGEN is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisations. In mixline specialisation some unfolding decisions depend on the outcome of tests performed at specialisation time instead of being hardwired into the specialiser

An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

Logical-Linguistic Model and Experiments in Document Retrieval

Conventional document retrieval systems have relied on the extensive use of the keyword approach with statistical parameters in their implementations. Now, it seems that such an approach has reached its upper limit of retrieval effectiveness, and therefore, new approaches should be investigated for the development of future systems. With current advances in hardware, programming languages and techniques, natural language processing and understanding, and generally, in the field of artificial intelligence, there are now attempts being made to include linguistic processing into document retrieval systems. Few attempts have been made to include parsing or syntactic analysis into document retrieval systems, and the results reported show some improvements in the level of retrieval effectiveness. The first part of this thesis sets out to investigate further the use of linguistic processing by including translation, instead of only parsing, into a document retrieval system. The translation process implemented is based on unification categorial grammar and uses C-Prolog as the building tool. It is used as the main part of the indexing process of documents and queries into a knowledge base predicate representation. Instead of using the vector space model to represent documents and queries, we have used a kind of knowledge base model which we call logical-linguistic model. A development of a robust parser-translator to perform the translation is discussed in detail in the thesis. A method of dealing with ambiguity is also incorporated in the parser-translator implementation. The retrieval process of this model is based on a logical implication process implemented in C-Prolog. In order to handle uncertainty in evaluating similarity values between documents and queries, meta level constructs are built upon the C-Prolog system. A logical meta language, called UNIL (UNcertain Implication Language), is proposed for controlling the implication process. Using UNIL, one can write a set of implication rules and thesaurus to define the matching function of a particular retrieval strategy. Thus, we have demonstrated and implemented the matching operation between a document and a query as an inference using unification. An inference from a document to a query is done in the context of global information represented by the implication rules and the thesaurus. A set of well structured experiments is performed with various retrieval strategies on a test collection of documents and queries in order to evaluate the performance of the system. The results obtained are analysed and discussed. The second part of the thesis sets out to implement and evaluate the imaging retrieval strategy as originally defined by van Rijsbergen. The imaging retrieval is implemented as a relevance feedback retrieval with nearest neighbour information which is defined as follows. One of the best retrieval strategies from the earlier experiments is chosen to perform the initial ranking of the documents, and a few top ranked documents will be retrieved and identified as relevant or not by the user. From this set of retrieved and relevant documents, we can obtain all other unretrieved documents which have any of the retrieved and relevant documents as their nearest neighbour. These unretrieved documents have the potential of also being relevant since they are 'close' to the retrieved and relevant ones, and thus their initial similarity values to the query will be updated according to their distances from their nearest neighbours. From the updated similarity values, a new ranking of documents can be obtained and evaluated. A few sets of experiments using imaging retrieval strategy are performed for the following objectives: to search for an appropriate updating function in order to produce a new ranking of documents, to determine an appropriate nearest neighbour set, to find the relationship of the retrieval effectiveness to the size of the documents shown to the user for relevance judgement, and lastly, to find the effectiveness of a multi-stage imaging retrieval. The results obtained are analysed and discussed. Generally, the thesis sets out to define the logical-linguistic model in document retrieval and demonstrates it by building an experimental system which will be referred to as SILOL (a Simple Logical-linguistic document retrieval system). A set of retrieval strategies will be experimented with and the results obtained will be analysed and discussed

Proof planning for logic program synthesis

The area of logic program synthesis is attracting increased interest. Most efforts have concentrated on applying techniques from functional program synthesis to logic program synthesis. This thesis investigates a new approach: Synthesizing logic programs automatically via middle-out reasoning in proof planning.[Bundy et al 90a] suggested middle-out reasoning in proof planning. Middleout reasoning uses variables to represent unknown details of a proof. Unifica¬ tion instantiates the variables in the subsequent planning, while proof planning provides the necessary search control.Middle-out reasoning is used for synthesis by planning the verification of an unknown logic program: The program body is represented with a meta-variable. The planning results both in an instantiation of the program body and a plan for the verification of that program. If the plan executes successfully, the synthesized program is partially correct and complete.Middle-out reasoning is also used to select induction schemes. Finding an appropriate induction scheme in synthesis is difficult, because the recursion in the program, which is unknown at the outset, determines the induction in the proof. In middle-out induction, we set up a schematic step case by representing the constructors applied to the induction variables with meta-variables. Once the step case is complete, the instantiated variables correspond to an induction appropriate to the recursion of the program.The results reported in this thesis are encouraging. The approach has been implemented as an extension to the proof planner CUM [Bundy et al 90c], called Periwinkle, which has been used to synthesize a variety of programs fully automatically