Kenneth D. Forbus

Qualitative Process Theory

Abstract Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and other things that cause changes in objects over time are intuitively characterized as processes . To understand commonsense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called the quantity space . This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string, but not push with it.

Compositional modeling: finding the right model for the job

Abstract To represent an engineers knowledge will require domain theories that are orders of magnitude larger than todays theories, describe phenomena at several levels of granularity, and incorporate multiple perspectives. To build and use such theories effectively requires strategies for organizing domain models and techniques for determining which subset of knowledge to apply for a given task. This paper describes compositional modeling , a technique that addresses these issues. Compositional modeling uses explicit modeling assumptions to decompose domain knowledge into semi-independent model fragments, each describing various aspects of objects and physical processes. We describe an implemented algorithm for model composition . That is, given a general domain theory, a structural description of a specific system, and a query about the systems behavior, the algorithm composes a model which suffices to answer the query while minimizing extraneous detail. We illustrate the utility of compositional modeling by outlining the organization of a large-scale, multi-grain, multi-perspective model we have built for engineering thermodynamics, and showing how the model composition algorithm can be used to automatically select the appropriate knowledge to answer questions in a tutorial setting.

Retrieval, reuse, revision and retention in case-based reasoning

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Qualitative spatial reasoning: the CLOCK project

Spatial reasoning is ubiquitous in human problem solving. Significantly, many aspects of it appear to be qualitative. This paper describes a general framework for qualitative spatial reasoning and demonstrates how it can be used to understand complex mechanical systems, such as clocks. The framework is organized around three ideas. (1) We conjecture that no powerful, general-purpose, purely qualitative representation of spatial properties exists (the poverty conjecture). (2) We describe the MD/PV model of spatial reasoning, which overcomes this fundamental limitation by combining the power of diagrams with qualitative spatial representations. In particular, a metric diagram, which combines quantitative and symbolic information, is used as the foundation for constructing a place vocabulary, a symbolic representation of shape and space which supports qualitative spatial reasoning. (3) We claim that shape and connectivity are the central features of qualitative spatial representations for kinematics. We begin by exploring these ideas in detail, pointing out why simpler representations have not proven fruitful. We also describe how inferences can be organized using the MD/PV model. We demonstrate the utility of this model by describing clock, a program which reasons about complex two-dimensional mechanisms. clock starts with a CAD description of a mechanisms parts and constructs a qualitative simulation of how it can behave. clock successfully performed the first complete qualitative simulation of a mechanical clock from first principles, a milestone in qualitative physics. We also examine other work on qualitative spatial reasoning, and show how it fits into this framework. Finally, we discuss new research questions this framework raises.

Qualitative physics: past, present, and future

Publisher Summary Qualitative physics is concerned with representing and reasoning about the physical world. The goal of qualitative physics is to capture both the commonsense knowledge of the person on the street and the tacit knowledge underlying the quantitative knowledge used by engineers and scientists. The key to qualitative physics is to find ways to represent continuous properties of the world by discrete systems of symbols. One can always quantize something continuous, but not all quantizations are equally useful. One way to state the idea is the relevance principle: The distinctions made by a quantization must be relevant to the kind of reasoning performed. This chapter describes what qualitative physics is, why one should be doing it, and where it came from. It discusses some open problems in qualitative physics.

The qualitative process engine

Abstract Efficient qualitative simulators are crucial to continued progress in qualitative physics. Assumption-based truth maintenance systems (ATMS) were developed in part to simplify writing such programs. This paper identifies several general abstractions for organizing ATMS-based problem-solvers which are especially useful for envisioning. In particular, we describe the many-worlds database , which avoids complex temporal reference schemes; how to organize problem-solving into justify/assume/interpret cycles which successively construct and extend partial solutions; and closed-world tables , which provide a mechanism for making closed-world assumptions. We sketch the design of the Qualitative Process Engine , QPE, an implementation of Qualitative Process theory, to illustrate the utility of these abstractions. On the basis of our experience in developing QPE and analyzing its performance, we draw some general conclusions about the advantages and disadvantages of assumption-based truth maintenance systems.

Towards a computational model of sketching

Sketching is a powerful means of interpersonal communication. While many useful multimodal systems have been created, current systems are far from achieving human-like participation in sketching. A computational model of sketching would help characterize these differences and help us better understand how to overcome them. This paper is a first step towards such a model. We start with an example of a sketching system(nuSketch COA Creator)designed to aid military planners, to provide context and a source of examples. We then describe four dimensions of sketching,visual understanding, conceptual understanding, language understanding,anddrawing,that can be used to characterize the competence of existing systems and identify open problems. The issues involved will be illustrated by examples from our experience with nuSketch. Three research challenges are posed, to serve as milestones towards a computational model of sketching that can explain and replicate human abilities in this area.

Qualitative kinematics: a framework

Qualitative spatial reasoning has seen little progress This paper attempts to explain why We provide a framework for qualitative kinematics (QK), qualitative spatial reasoning about motion We propose that no general-purpose, purely qualitative kinematics exists. We propose instead the MD/PV model of spatial reasoning, which combines the power of diagrams with qualitative representations Next we propose connectivity as the organizing principle for kinematic state, and describe a set of basic inferences which every QK system must make. The frameworks utility is illustrated by considering two programs, one finished and one in progress We end by discussing the research questions this framework raises.

Setting up large scale qualitative models

A qualitative physics which captures the depth and breadth of an engineers knowledge will be orders of magnitude larger than the models of todays qualitative physics. To build and use such models effectively requires explicit modeling assumptions to manage complexity. This, in turn, gives rise to the problem of selecting the right qualitative model for some purpose. This paper addresses these issues by describing a set of conventions for modeling assumptions. Simplifying assumptions decompose a domain into different grain sizes and perspectives which may be reasoned about separately. Operating assumptions reduce the complexity of qualitative simulation by focusing on particular behaviors of interest. We show how these assumptions can be directly represented in Qualitative Process theory, using a multi-grain, multi-slice model of a Navy propulsion plant for illustration. Importantly, we show that model selection can often be performed automatically via partial instantiation. We illustrate this technique with a simple explanation generation program that uses the propulsion plant model to answer questions about physical and functional characteristics of its operation.

CogSketch: Sketch Understanding for Cognitive Science Research and for Education

Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketch-based educational software. We describe the idea of open-domain sketch understanding, the scientific hypotheses underlying CogSketch, and provide an overview of the models it employs, illustrated by simulation studies and ongoing experiments in creating sketch-based educational software.