QP IS MORE THAN SPQR AND DYNAMICAL SYSTEMS THEORY: RESPONSE TO SACKS AND DOYLE

ions and generalization rules. For example, a human might be able to use the knowledge that reaching the same state again and again means that the system is in a cyclic state, and may make the inferential jump, even without advanced mathematical knowledge about dynamical systems. The QP calculi seem to have been designed under some implicit constraints, namely, that they display some of the perceived properties of human reasoning about the physical world: that humans often appear to combine causal relations recursively, and in cases where they have the structure of the physical system available, trace the topology of the physical system to follow the “flow of causality.” I will argue that QP techniques should aim to use the heuristic power of human reasoning even more, while employing the power of formal analysis to clearly defined subproblems where such techniques are needed. Thus, the issue is broadened to include: What should the connection of QP research be to human commonsense knowledge and reasoning about the physical world? Is Newtonian physical modeling sufficient for QP, or necessary for all the goals of QP? If one were only interested in producing a technology that assists in reasoning about the physical world, can one develop this technology without to some degree being concerned with human commonsense knowledge and reasoning methods? My concern is to ensure that qualitative physics 218 COMPUTATIONAL INTELLIGENCE research has a significant place not only for mathematically sophisticated analysis techniques as S&D propose, but also for a whole spectrum of issues concerning the sources of the power in human reasoning about the physical world. 3. HUMAN QUALITATIVE REASOMNG ABOUT THE PHYSICAL WORLD A trained physicist and an unschooled man-on-the-street start with a common ontology and a shared cognitive architecture. The physicist learns, and may add to, a specialized ontology as well, and acquires a number of modeling and analytical techniques. We need to sort out these distinct types of knowledge about the physical world that come into play in human reasoning. 1 . A commonsense ontology which predates and is in fact used by modem science: space, time, flow, physical objects, cause, state, perceptual primitives such as shapes, and so on. The commonsense ontology also comes with some terms that are given specific technical meanings by science, but in general the terms in this ontology are experientially and logically so fundamental that scientific theories are built on the infrastructure of this ontology. Early work in QP had as a main goal elaboration of such an ontology (Hayes 1979 and Forbus 1984 are examples). Even today, a good deal of QP research grapples with the development of ontologies for different parts of commonsense physical knowledge. 2. The scientific ontology is built on the commonsense ontology (and often gives specific technical meanings to some of the terms in it, such as “force”). Additional concepts and terms are constructed. Some of these are quite outside commonsense experience (examples are “voltage,” “current,” and “charm of quarks”). 3. Compiled causal knowledge. People compile causal expectations partly from direct experience and partly by caching some results from earlier problem solving. Which causal expectations get stored and used is largely determined by the relevance of the causes and effects to the goals of the problem solver. There is a more organized form of causal knowledge that we build up as well: models of causal processes. By process model I mean a description in terms of temporally evolving state transitions, where the state descriptions are couched using the commonsense and scientific ontologies. For example, we have commonsense causal processes such as “boiling,” or specialized ones such as “voltage amplification,” “the business cycle,” and so on. These are not neutral, agent-independent, process descriptions, but ones in which the qualitative states that participate in the description have been chosen based on abstractions of interest to the agent. In particular, such descriptions are couched in terms of possible intervention options on the world to affect the causal process, or observations to detect the process. Forbus’ processes (1984) and me and my colleagues’ work on functional representations (Sembugamoorthy and Chandrasekaran 1988; Goel 1989; Keuneke 1991 ; Sticklen and Tufankji 1991) are examples concerned with the development of representations for causal processes. When the process model is based on prescientific or unscientific views, we have naive process models (such as models of sun rotating around the earth, or of exorcism of evil spirits). Many prescientific process models are not only quite adequate, but are actually simpler and more computationally efficient than the scientific ones, for everyday purposes. These process descriptions are great organizing aids: they focus the direction of prediction, help in the identification of structures to realize desired functions in design (Goel 1989), and suggest actions to enable or abort the process. QP IS MORE THAN SPQR AND DYNAMICAL SYSTEMS THEORY 219 4. Mathematical equations embodying scientific laws and expressing relations between state variables. These equations themselves are acausal, and any causal direction is given by additional knowledge about which variables are exogenous. 4. SOME OF THE THINGS THAT A NEW QP SHOULD INCLUDE It is generally agreed, including by S&D, that a QP theory or framework should provide support for three components of reasoning about the physical world: modeling, prediction and control. In fact, a weakness of their paper is that they pay only lip service to the problem of modeling and fail to show why or how dynamic analysis will help solve that and the control problems. With the recent exception of SPQR calculi, quite a bit of the work in QP research is concerned with the development of ontologies, which are directly relevant to the modeling problem. Since I expect other respondents to outline precisely how the QP field is paying attention to these problems, I will concentrate on those aspects of the problem unlikely to be emphasized by them.