Qualitative models arising in the artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill-understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning. Categories and Subject Descriptors: I.2.4 [Artificial Intelligen...

Present day systems, intelligent or otherwise, are limited by the conceptualizations of the world given to them by their designers. In this paper, we propose a novel, first-principles approach to performing incremental reformulations for computational efficiency. First, we define a reformulation to be a shift in conceptualization: a change in the basic objects, functions, and relations assumed in a formulation. We then analyze the requirements for automating reformulation and show the need for justifying shifts in conceptualization. Inefficient formulations make irrelevant distinctions. A new class of meta-theoretical justifications for a reformulation called irrelevance explanations, is presented. A logical irrelevance explanation demonstrates that certain distinctions made in the formulation are not necessary for the computation of a given class of problems. A computational irrelevance explanation shows that some distinctions are not useful with respect to a given problem solver fo...

Traditional computer simulation terminology includes taxonomic divisions with terms such as "discrete event," "continuous," and "process oriented." Even though such terms have become familiar to simulation researchers, the terminology is distinct from other disciplines ---such as artificial intelligence and software engineering--- which have similar goals relating specifically to modelling dynamic systems. There is a need to unify terminology among these disciplines so that system modelling is formalized in a common framework. We present a perspective that serves to characterize simulation models in terms of their procedural versus declarative orientations since these two orientations are prevalent throughout most modelling disciplines that we have encountered. We used a sample dynamic system (e.g., two jug problem) found in artificial intelligence to highlight the connecting threads in system modelling within each discipline. Moreover, in teaching simulation students using this perspe...

Time is a central factor in patient monitoring. Introduction of domain-dependent knowledge is essential to ensure efficiency of time managers especially when embedded into systems that interact with real world. We present a realistic temporal reasoning model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgetting

Interval narrowing techniques are a key issue for handling constraints over real numbers in the logic programming framework. However, the standard fixed-point algorithm used for interval narrowing may give rise to cyclic phenomena and hence to problems of slow convergence. Analysis of these cyclic phenomena shows: 1) that a large number of operations carried out during a cycle are unnecessary; 2) that many others could be removed from cycles and performed only once when these cycles have been processed. What is proposed here is a revised interval narrowing algorithm for identifying and simplifying such cyclic phenomena dynamically. First experimental results show that this approach improves performance significantly.

this paper is on the first problem. It shows that there is a strong connection between the existence of cyclic phenomena and slow convergence. The main goal is to dynamically identify cyclic phenomena while executing algorithm

This article is concerned with the general question of how to represent biological knowledge in computers such that it may be used in multiple problem solving tasks. In particular, I present a model of a bacterial gene regulation system that is used by a program that simulates gene regulation experiments,

Explanation-based learning (EBL) systems have established their applicability to a wide variety of tasks. However, in despite intensive research, several problems relating to explanation-based learning have remained by and large open. This paper describes an approach to the problems of generalizing number and learning efficiently from multiple examples. The basic insight upon which the technique is based is that EBL can be thought of as learning control knowledge for a theorem-prover. By providing a richer representation for such control knowledge, more general rules can be learned: in particular, by providing looping constructs, rules which generalize number can be expressed; and by providing conditional branches, rules learned from different training examples can be combined. The technique described has been fully implemented, is domain-independent, and has been applied to a number of examples from the domain of VLSI circuit design.

A general problem in qualitative physics is determining the consequences of assumptions about the behavior of a system. If the space of behaviors is represented by an envisionment, many such consequences can be represented by pruning states from the envisionment. This paper provides a formal logic of occurrence which justifies the algorithms involved and provides a language for relating specific histories to envisionments. The concepts and axioms are general enough to be applicable to any system of qualitative physics. We further propose the concept of transverse quantities as a general solution to qualitative versions of Zeno's paradox. The utility of these ideas is illustrated by a rational reconstruction of the pruning algorithms used in FROB, a working AI program. December, 1 Introduction A goal of qualitative physics is to predict the behavior of physical systems. One technique, envisioning, generates all possible behaviors of a system, relative to a particular set of backgroun...

Understanding people's commonsense knowledge about physical world is a fundamental problem in building intelligent systems. If this knowledge can be represented and used by computers, they can duplicate people's ability to understand and interact with the world. Qualitative physics is the attempt to capture and formalize this knowledge. An important aspect of qualitative reasoning is the ability to derive the possible behaviors of a given physical system from the structure of the system, using minimal initial information. This thesis investigates qualitative domain theories and reasoning techniques which will en-able computers to analyze the qualitative behaviors of physical systems which include both me-chanical mechanisms and fluids, such as internal combustion engines and hydraulic lift pumps. We have developed a domain theory which integrates richer models of mechanics, fluids, and geometry than previous research in qualitative physics. These theories and inference techniques are embodied in QSA, a program that produces possible behaviors of physical systems. iii To My Parents iv I would like to thank: ACKNOWLEDGMENTS • Professor Kenneth Forbus, my advisor, for initiating and directing this research, for stim-ulating me to think creatively and to stand on my own legs, and for giving me a great example of researcher.