International Journal on Artificial Intelligence Tools

Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.

Associative computation is characterized by seamless intertwining of search-by-content and data parallel computation. The search-by-content paradigm is natural to scalable high performance heterogeneous computing since the use of tagged data avoids the need for explicit addressing mechanisms. In this paper, the author presents an algebra for associative logic programming, an associative resolution scheme, and a generic framework of an associative abstract instruction set. The model is based on the integration of data alignment and the use of two types of bags: data element bags and filter bags of Boolean values to select and restrict computation on data elements. The use of filter bags integrated with data alignment reduces computation and data transfer overhead, and the use of tagged data reduces overhead of preparing data before data transmission. The abstract instruction set has been illustrated by an example. Performance results are presented for a simulation in a homogeneous address space.

In order to improve constraint satisfaction techniques, a promising way is to use special features of constraints and variable domains. This paper examines a special class of CSPs (Constraint Satisfaction Problems) in which a partial order is defined on each domain and the constraints are compatible with these orders. A special arc-consistency algorithm for this case is presented and some questions about finding all solutions are discussed. The presented ideas can be used in constraint systems implemented in an object-oriented language, where inheritance hierarchy of objects is a natural support for the presented CSP type.

This paper describes a new approach to system modeling by using a neo-fuzzy-neuron. The target system is modeled adaptively by simply feeding its input and output data to the neo-fuzzy-neuron, whose basic principle was proposed by the authors in 1992. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then enhanced in order to get a better generalization capability. The enhanced version of the neo-fuzzy-neuron could facilitate to identify clearly the nonlinear chaotic system, and could most likely predict the future behavior of the system. The neo-fuzzy-neuron was further applied to the identification of actual sound insulation systems. The application was a success and provided promising results.

An appropriate combination of symbolic and numeric solvers often makes it possible to solve problems that none of these solvers can tackle alone. In this paper, we specify a cooperative architecture which allows using concurrently heterogeneous solvers when handling constraints over the reals. This architecture is based upon agents that communicate via asynchronous message passing. Agents are synchronized when a failure or a success occurs. Disjunctive constraints are handled by backtracking. Operational semantics and terminating conditions of such systems are discussed. Implementation issues are addressed. We end the presentation by several examples and give some computational results from a first prototype.

For learning tasks with few examples, greater classification accuracy can be achieved by learning several concept descriptions for each class in the data and producing a classification that combines evidence from multiple descriptions. Stochastic (randomized) search can be used to generate many concept descriptions for each class. Here we use a tractable approximation to the optimal Bayesian method for combining evidence from multiple descriptions. Learning multiple descriptions is very useful when additional data is difficult to obtain. The primary result of this paper is that multiple concept descriptions are particularly helpful for improving accuracy in hypothesis spaces in which there are many equally good rules to learn. Another result is experimental evidence that learning multiple rule sets yields more accurate classifications than learning multiple rules for concepts containing many disjuncts.

This paper presents the theory and implementation of a logic based discrete event simulation system ECSIM, Event Calculus SIMulation. ECSIM’s representation language is PROLOG extended with temporal predicates derived from the event calculus. The theory defines the truth value of a property given a history of events. ECSIM can represent actions that happen at a particular point in time, and activities that happen over a period of time; it can represent properties that change discretely and continuously over time. ECSIM’s scheduling algorithm uses activity scanning to generate event notices for all future activities. ECSIM’s major distinction with other simulation systems is its reference to the complete history of simulated time. A given event schedule can be analyzed by deriving the properties of the world at any time in the simulated history. ECSIM’s logic programming framework enables classical simulation to be extended with explanation generation, inductive learning, planning, decision support, simulation of intelligent agents, and symbolic simulation.

In this paper we study the issue of providing intelligent answers to recursive queries initially proposed in [Imie88]. In contrast to conventional query answers, which are sets of tuples, intelligent answers are rules describing the characteristics of the results. This way, an intelligent answer facilitates users to better capture the meaning of the query.

To this aim, we first extend the notion of intelligent answers by allowing them to be partial (or incomplete). Complete and partial intelligent answers are both useful, in that they provide (possibly partial) characterizations of queries. We will show that complete intelligent answers also give a more efficient way to evaluate queries. For constructing intelligent answers, we propose a general approach that enables us to combine several cases studied in [Imie88].

Moreover, we will show that semantic constraints can be systematically utilized to provide complete intelligent answers to a wider range of queries. We consider two commonly used classes of semantic constraints called implication and referential constraints, and prove that the completeness of intelligent answers with respect to semantic constraints can be reduced to the semantics-based query containment problem. Finally, a necessary and sufficient condition is given to solve this containment problem.

The concepts of maintenance sequences of a knowledge base and their limits are introduced. Some concepts used in maintenance of knowledge bases, such as new laws, user’s rejections, and reconstructions of a base are defined; the related theorems are proved. A maintenance procedure scheme is defined. The maintenance sequences generated by the procedure are convergent, and their limits are the set of true sentences of the model. Some computational aspects of reconstructions are studied; an R-calculus is given to deduce a reconstruction when a knowledge base meets a rejection. Especially, an R-calculus for logic programming is provided and implemented in Prolog. Finally, our research is compared with AGM’s theory of belief revision and other relevant work.

In this paper we describe a purely declarative method for introducing modularity into forward-chaining, rule-based languages. The method is enforced by the syntax of the language and includes the ability to parameterize the rule groups. We also describe the Venus programming environment, which implements the presented ideas. Drawing from two of three Venus applications developed to date, we illustrate how this form of modularity contributes directly to the resolution of certain software engineering problems associated with rule languages. We also discuss key implementation details and present performance data.

In this paper, we present a development framework and verification methodologies for knowledge-based systems (KBSs) with real-time systems as our target system. The framework originates from an integration of three software development paradigms: rapid prototyping, operational specification, and transformational implementation. Based on this framework, we present RT-FRORL as a formal requirements specification language which exploits knowledge representation techniques as an aid in the specification, development, and verification of a KBS for real-time systems. RT-FRORL uses a combination of resolution refutation, anomaly detection matrix, and algorithms methods to verify a number of properties which might exist in KBSs. By incorporating RT-FROHL and its verification methods with the underlying framework, it lays a very strong foundation to deal with the current issues in KBSs verification.

As program behavior becomes complex, it’s increasingly important to analyze their behavior statistically. The article describes two separate but synergistic tools for statistically analyzing large Lisp programs. The first tool, called CLIP (Common Lisp Instrumentation Package), allows the researcher to define and run experiments, including experimental conditions (parameter values of the planner or simulator) and data to be collected. The data are written out to data files that can be analyzed by statistics software. The second tool, called CLASP (Common Lisp Analytical Statistics Package), allows the researcher to analyze data from experiments by using graphics, statistical tests, and various kinds of data manipulation. CLASP has a graphical user interface (using CLIM, the Common Lisp Interface Manager) and also allows data to be directly processed by Lisp functions. Finally, the paper describes a number of other data-analysis modules that have been added to work with CLIP and CLASP.

We describe two domain-independent temporal reasoning systems called TimeGraph I and II which can be used in AI applications as tools for efficiently managing large sets of relations in the Point Algebra, in the Interval Algebra, and metric information such as absolute times and durations. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. TimeGraph I was originally developed by Taugher, Schubert and Miller in the context of story comprehension. TimeGraph II provides useful extensions, including efficient algorithms for handing inequations, and relations expressing point-interval exclusion and interval disjointness. These extensions make the system much more expressive in the representation of qualitative information and suitable for a large class of applications.