simulation@uflorida.cis.ufl.edu (Moderator: Paul Fishwick) (09/19/88)
Volume: 5, Issue: 2, Mon Sep 19 11:31:33 EDT 1988 +----------------+ | TODAY'S TOPICS | +----------------+ SPECIAL ISSUE: Commonsense Reasoning/Continuous Actions Moderator: Paul Fishwick, Univ. of Florida Send topical mail to: simulation@uflorida.cis.ufl.edu ----------------------------------------------------------------------------- >From dambrosi@mist.cs.orst.edu Fri Sep 16 23:43:22 1988 Date: Fri, 16 Sep 88 20:43:01 pdt From: Bruce D'Ambrosio <dambrosi@mist.cs.orst.edu> To: fishwick@fish.cis.ufl.edu Subject: Re: SIMULATION DIGEST V5 N1 > Has anyone seen a commonsense model that is superior to differential equations, ...? There are several problems here. Imagine attempting to build an autonomous agent that can apply "commonsense " physical knowledge to reason about situations it encounters. What would it have to do? I propose roughly the following scenario (assuming it has first tried some form of case-based or experiential reasoning, which I ingore for the present discussion, but in no way discount the importance of): 1. Chose an appropriate domain theory given the current situation, goals, etc. 2. Segment the world situation appropriately. 3. Build a model of the world situation in some formal mathematics. 4. Solve the mathematical system. 5. Interpret the results in terms of the world situation being faced. (This is only very approximate, and I do not mean to imply strict sequentiallity of the steps - especially 1 and 2) When we look at this larger problem, we see that classical physics offers several theories, but no formal mechanism for choosing an appropriate one. It says nothing about segmenting, or how to construct a formal model given a theory, a mathematics, and a segmentation. Mathematics provides lots of techniques for solving systems of equations if you chose standard quantitative math, but again, there are no formal methods (although lots of informal ones) for "interpreting" the resulting solutions physically. Most of the interesting work in "commonsense physics" can be seen as addressing items above that are completely ignored (at least in terms of providing formal, automatable techniques) by physics (Forbus, especially, worries about segmentation (defviews and contingent individuals) and extending the range of mathematics available (deKleers work on confluences, and William's work to provide a new qualitative algebra, esp), to better match the range of situations in terms of data available, precision of results required, etc. ------------------------------ [anonymous] The problem you raise, that of modeling continuous/dynamic systems, is addressed in new ways (newer than differential equations) by the folks working in "qualitative physics" and "qualitative simulation". In particular, I call your attention to the book "Qualitative Reasoning About Physical Systems", edited by Daniel G. Bobrow, 1985, MIT Press. Of several people working in this field, the one whose work is most closely related to modern control theory (which uses coupled first-order differential equations) is Benjamin Kuipers. His QSIM algorithm (Artificial Intelligence, Vol. 29, No. 3) uses couple first-order _qualitative_ differential equations. Kuipers' QSIM and Forbus' QPE, for example, are both used for modeling continuous physical systems in a qualitative way. ------------------------------ Article 1679 of comp.ai: Path: uflorida!haven!ames!ucsd!nosc!humu!uhccux!lee From: lee@uhccux.uhcc.hawaii.edu (Greg Lee) Newsgroups: comp.ai,sci.math,sci.lang Subject: Re: state and change/continuous actions Date: 17 Sep 88 16:14:13 GMT References: <18249@uflorida.cis.ufl.EDU> Organization: University of Hawaii Xref: uflorida comp.ai:1679 sci.math:2843 sci.lang:1964 >From article <18249@uflorida.cis.ufl.EDU>, by fishwick@uflorida.cis.ufl.EDU (Paul Fishwick): " " 2) If commonsense knowledge representation is the issue then we " might want to ask a fundamental question "Why do we care about " representing commonsense knowledge about continuous actions?" " I can see 2 possible goals: One goal is to validate some given " ... To reason about continuous actions where the physics hasn't been worked out or is computationally infeasible. How about that as a third goal? " Obviously, I'm trying to spark some inter-group discussion and so I hope " that any responses will post to both the AI group (comp.ai) AND " the SIMULATION group (comp.simulation). In addition (sci.math) and " (comp.theory.dynamic-sys) may be appropriate. Tsk, tsk. Left out sci.lang. The way people think about these things is reflected in the tense/aspect systems of natural languages. " I believe that Genesereth and Nilsson are quite correct that "reasoning " about time and continous actions" is an important issue. However, an " even more important issue revolves around people discussing " concepts about "state," "time," and "change" by crossing disciplines. " Any thoughts? In English, predicates which can occur with Agent subjects, those capable of deliberate action, can also occur in the progressive aspect, expressing continuous action. This suggests some connection between intent and continuity whose nature is not obvious, to me anyway. Greg, lee@uhccux.uhcc.hawaii.edu ------------------------------ Article 1685 of comp.ai: Path: uflorida!mailrus!cornell!uw-beaver!teknowledge-vaxc!sri-unix!garth!smryan From: smryan@garth.UUCP (Steven Ryan) Newsgroups: comp.ai,sci.math Subject: Re: state and change/continuous actions Date: 19 Sep 88 01:18:29 GMT References: <18249@uflorida.cis.ufl.EDU> Reply-To: smryan@garth.UUCP (Steven Ryan) Organization: INTERGRAPH (APD) -- Palo Alto, CA Xref: uflorida comp.ai:1685 sci.math:2851 >Foundations of Artificial Intelligence," I find it interesting to >compare and contrast the concepts described in Chapter 11 - "State >and Change" with state/change concepts defined within systems >theory and simulation modeling. The authors make the following statement: >"Insufficient attention has been paid to the problem of continuous >actions." Now, a question that immediately comes to mind is "What problem?" Presumably, they are referring to that formal systems are strictly discrete and finite. This has to do to with `effective computation.' Discrete systems can be explained in such simple terms that is always clear exactly what is being done. Continuous systems are computably using calculus, but is this `effective computation?' Calculus uses a number of existent theorems which prove some point or set exists, but provide no method to effectively compute the value. Or is knowing the value exists sufficient because, after all, we can map the real line into a bounded interval which can be traversed in finite time? It is not clear that all natural phenomon can be modelled on the discrete and finite digital computer. If not, what computer could we use? >Any thoughts? ------------------------------ >From JMC@SAIL.Stanford.EDU Sun Sep 18 18:42:57 1988 Date: 18 Sep 88 1543 PDT From: John McCarthy <JMC@SAIL.Stanford.EDU> Subject: common sense knowledge of continuous action To: fishwick@BIKINI.CIS.UFL.EDU, ailist@AI.AI.MIT.EDU If Genesereth and Nilsson didn't give an example to illustrate why differential equations aren't enough, they should have. The example I like to give when I lecture is that of spilling the water glass on the lectern. If the front row is very close, it might get wet, but usually not even that. The Navier-Stokes equations govern the flow of the spilled water but are entirely useless in this common sense situation. No-one can acquire the initial conditions or integrate the equations sufficiently rapidly. Moreover, absorbtion of water by the materials it flows over is probably a strong enough effect, so that more than the Navier-Stokes equations would be necessary. Thus there is no "scientific theory" involving differential equations, queuing theory, etc. that can be used by a robot to determine what can be expected when a glass of water is spilled, given what information is actually available to an observer. To use the terminology of my 1969 paper with Pat Hayes, the differential equations don't form an epistemologically adequate model of the phenomenon, i.e. a model that uses the information actually available. While some people are interested in modelling human performance as an aspect of psychology, my interest is artificial intelligence. There is no conflict with science. What we need is a scientific theory that can use the information available to a robot with human opportunities to observe and do as well as a human in predicting what will happen. Thus our goal is a scientific common sense. The Navier-Stokes equations are important in (1) the design of airplane wings, (2) in the derivation of general inequalities, some of which might even be translatable into terms common sense can use. For example, the Bernouilli effect, once a person has (usually with difficulty) integrated it into his common sense knowledge can be useful for qualitatively predicting the effects of winds flowing over a house. Finally, the Navier Stokes equations are imbedded in a framework of common sense knowledge and reasoning that determine the conditions under which they are applied to the design of airplane wings, etc. ------------------------------ Date: Mon, 19 Sep 88 11:24:23 EDT From: Paul Fishwick <fishwick> To: fishwick@fish I very much appreciate Prof. McCarthy's response and would like to comment. The "water glass on the lectern" example is a good one for commonsense reasoning; however, let's further examine this scenario. First, if we wanted a highly accurate model of water flow then we would probably use flow equations (such as the NS equations) possibly combined with projectile modeling. Note also that a lumped model of the detailed math model may reduce complexity and provide an answer for us. We have not seen specific work in this area since spilt water in a room is of little scientific value to most researchers. Please note that I am not trying to be facetious -- I am just trying to point out that *if* the goal is "to solve the problem of predicting the result of continuous actions" then math models (and not commonsense models) are the method of choice. Note that the math model need not be limited to a single set of PDE's. Also, the math model can be an abstract "lumped model" with less complexity. The general method of simulation incorporates combined continuous and discrete methods to solve all kinds of physical problems. For instance, one needs to use notions of probability (that a water will make it to the front row), simplified flow equations, and projectile motion. Also, solving of the "problem of what happens to the water" need not involve flow equations. Witness, for instance, the work of Toffoli and Wolfram where cellular automata may be used "as an alternative to" differential equations. Also, the problem may be solved using visual pattern matching - it is quite likely that humans "reason" about "what will happen" to spilt liquids using associative database methods (the neural netlanders might like this approach) based on a huge library of partial images from previous experience (note Kosslyn's work). I still haven't mentioned anything about artificial intelligence yet - just methods of problem solving. I agree that differential equations by themselves do not comprise an epistemologically adequate model. But note that no complex problem is solved using only one model language (such as DE's). The use of simulation is a nice example since, in simulating a complex system, one might use many "languages" to solve the problem. Therefore, I'm not sure that epistemological adequacy is the issue. The issue is, instead, to solve the problem by whatever methods available. Now, back to AI. I agree that "there is no theory involving DE's (etc.) that can be used by a robot to determine what can be expected when a glass of water is spilled." I would like to take the stronger position that searching for such a singular theory seems futile. Certainly, robots of the future will need to reason about the world and about moving liquids; however, we can program robots to use pattern matching and whatever else is necesssary to "solve the problem." I supposed that I am predisposed to an engineering philosophy that would suggest research into a method to allow robots to perform pattern recognition and equation solving to answer questions about the real world. I see no evidence of a specific theory that will represent the "intelligence" of the robot. I see only a plethora of problem solving tools that can be used to make future robots more and more adaptive to their environments. If commonsense theories are to be useful then they must be validated. Against what? Well, these theories could be used to build programs that can be placed inside working robots. Those robots that performed better (according to some statistical criterion) would validate respective theories used to program them. One must either 1) validate against real world data [the cornerstone to the method of computer simulation] , or 2) improved performance. Do commonsense theories have anything to say about these two "yardsticks?" Note that there are many AI research efforts that have addressed validation - expert systems such as MYCIN correctly answered "more and more" diagnoses as the program was improved. The yardstick for MYCIN is therefore a statistical measure of validity. My hat is off to the MYCIN team for proving the efficacy of their methods. Expert systems are indeed a success. Chess programs have a simple yardstick - their USCF or FIDE rating. This concentration of yardsticks and method of validation is not only helpful, it is essential to demonstrate the an AI method is useful. -paul +------------------------------------------------------------------------+ | Prof. Paul A. Fishwick.... INTERNET: fishwick@bikini.cis.ufl.edu | | Dept. of Computer Science. UUCP: gatech!uflorida!fishwick | | Univ. of Florida.......... PHONE: (904)-335-8036 | | Bldg. CSE, Room 301....... FAX is available | | Gainesville, FL 32611..... | +------------------------------------------------------------------------+ ------------------------------ +--------------------------+ | END OF SIMULATION DIGEST | +--------------------------+