sapong@dvinci.usask.ca (Kofi Sapong) (08/10/90)
If anyone has any information on expert system shells that use the Dempster-Shafer approach to handle uncertainty, could he/she kindly pass it on to me. On the other hand if you have or know of any program that implements any form of a propagation scheme using the above approach, I will interested in having it. Thank you. Kofi Sapong University of Saskatchewan Canada. e-mail: sapong@skorpio.usask.ca or sapong@dvinci.usask.ca
ahlenius@motcid.uucp (Mark Ahlenius) (08/14/90)
sapong@dvinci.usask.ca (Kofi Sapong) writes: >If anyone has any information on expert system shells that use the >Dempster-Shafer approach to handle uncertainty, could he/she kindly >pass it on to me. On the other hand if you have or know of any program >that implements any form of a propagation scheme using the above >approach, I will interested in having it. Maybe someone else can comment on this - but I thought that the Depster- Schafer theory was considered dead, and that all work was now being done in fuzzy and Bayesian type theories. Please correct me if wrong, this is my impression from talking with others at the AAAI-90. -- =============== regards 'mark ============================================= Mark Ahlenius voice:(708)-632-5346 email: uunet!motcid!ahleniusm Motorola Inc. fax: (708)-632-2413 Arlington, Hts. IL, USA 60004
timm@runxtsa.runx.oz.au (Tim Menzies) (08/15/90)
In article <6390@uklirb.informatik.uni-kl.de> sapong@dvinci.usask.ca (Kofi Sapong) writes: >If anyone has any information on expert system shells that use the >Dempster-Shafer approach to handle uncertainty, could he/she kindly >pass it on to me. On the other hand if you have or know of any program >that implements any form of a propagation scheme using the above >approach, I will interested in having it. I got real excited about D-M a while back. I chased up the original D-M article "Evidential Reasoning in a Hierarchy" by Gordon J. and Shortliffe E.H. in Artificial Intelligence 26 1985, 323-357. On page 350 of that article I read: We have previoulst suggested in fact that the details of a model of evidential reasoning in an AI system may be relatively unimportant since the careful semantic structuring of a domain's knowledge seems to blunt the sensitivity of the inferences to the values of the numbers used. Translation: topology and not clever combination rules make for a smart system. I've heard anecdoctal stories of people performing sensitivity analysis on systems where they tinkered with the certaintity factor combination rules, threseholds of belief, etc, and found that that their semantic nets were less sensitive to the numbers than to the connections within the net. I've seen myself a study in which someone ran a set of rules for black-jack and deduced what stragegies to follow based on various ways of combining numeric certainty factors. They tried four ways, including straight averaginh of certainty factors for the conclusions. This averaging method was included as the "straw-man". It was intended to show how poor this method was compared to (say) the sophistication of the MYCIN-style certainty factor kludge. Surprise, surprise, straight averaging woked just as well as anything else. So, I'm not so interested in D-M anymore. Now I seek ways to organise the topology of my categorical and-or nets. -- _--_|\ Tim Menzies (timm@runxtsa.oz) PH: 02 9297729, 61 2 4280200 (fax) / \ HiSoft Expert Systems Group, "Software should be altered to \_.--._/ 2-6 Orion Rd Lane Cove, NSW, accomodate the quirks of human v Australia, 2066 thought, and not vice versa."
mgv@usceast.UUCP (Marco Valtorta) (08/18/90)
In article <6441@uklirb.informatik.uni-kl.de> ahlenius@motcid.uucp (Mark Ahlenius) writes: >sapong@dvinci.usask.ca (Kofi Sapong) writes: > >>If anyone has any information on expert system shells that use the >>Dempster-Shafer approach to handle uncertainty, could he/she kindly >>pass it on to me. On the other hand if you have or know of any program >>that implements any form of a propagation scheme using the above >>approach, I will interested in having it. > >Maybe someone else can comment on this - but I thought that the Depster- >Schafer theory was considered dead, and that all work was now being >done in fuzzy and Bayesian type theories. Please correct me if wrong, >this is my impression from talking with others at the AAAI-90. >-- IMHO, you are wrong. There were papers on both the Bayesian and Dempster-Shafer approaches at AAAI-90 and at the Uncertainty in AI (UAI) conference that took place just before AAAI-90 at MIT. (For example, there are at least two articles on the Dempster-Shafer approach in the AAAI-90 proceedings, by Halpern and Fagin (pp.112-119) and by Hsia (pp. 120-125).) In fact, I was somewhat surprised to see that Dempster-Shafer seems to be alive and well. I attended the closing hours of UAI and witnessed an argument between Judea Pearl and Philippe Smets. I will try to give an impressionistic sketch of what happened. Smets had presented a paper on one of the examples presented by Pearl in his book, supposedly to show the inadequacy of Dempster-Shafer. Pearl claimed that the proposed solution required too much designer intervention. Smets counterargued by working out his reply in semi-real time. Pearl listed some pitfalls of non-monotonic logics that, he claimed, were also pitfalls of Dempster-Shafer. Smets replied that there was no evidence that those pitfalls could not be avoided in Dempster-Shafer. He also counterattacked when, in reply to another participant, he pointed out how fragile the identification of implication and conditional probability is. Smets also described an ESPRIT basic research action on uncertain reasoning called DRUMS ("Defeasible Reasoning and Uncertainty Management Systems"), which includes research on non-monotonic logics, possibility theory, and (Dempster-Shafer) belief functions, but in which Bayesian belief networks have no part. Since ESPRIT funded a 45 person-years project that included the development of a large Bayesian network (MUNIN), it seems that at least one major funding organization has not decided to abandon Dempster-Shafer. (Incidentally, a spin-off of the MUNIN project is the shell HUGIN, the best shell for belief network building on the market today.) I hope others will follow up on this topic! Maybe someone at the funding agencies? Is Maria Zemankowa at NSF tuned into this newsgroup? >=============== regards 'mark ===================================== >Mark Ahlenius voice:(708)-632-5346 email: uunet!motcid!ahleniusm >Motorola Inc. fax: (708)-632-2413 >Arlington, Hts. IL, USA 60004 Marco Valtorta usenet: ...!ncrcae!usceast!mgv Department of Computer Science internet: mgv@cs.scarolina.edu University of South Carolina tel.: (1)(803)777-4641 Columbia, SC 29208 tlx: 805038 USC U.S.A. fax: (1)(803)777-3065 usenet from Europe: ...!mcvax!uunet!ncrlnk!ncrcae!usceast!mgv
almond@alta.stat.washington.edu (Russell Almond) (10/04/90)
On shells available using Dempster-Shafer theory: I am the developer of a package of Common Lisp functions for implementing the belief function (Dempster-Shafer) theory. The program is called BELIEF and it operates in both the belief function and ordinary (Bayesian) probability modes (in the latter case it implements the Lauritzen and Spiegelhalter algorithm (JRSS, Series B, 1988). It is available via anonymous ftp from hustat.harvard.edu. It is still currently in a test state (as a matter of fact, I have corrected some bugs which I have not yet posted), but it works fairly well. The operator end of the user interface is very primitive (I'm a statistician, not a computer scientist), but the tools available for specifying knowledge bases are well designed. A TeX version of the operator's manual can be gotten from the same source. It can also be optained from Harvard University, Departement of Statistics (Technical Report S-128). A detailed description of the algorithms involved is in my disseration (Tech. Report S-130). As I am interested in pursing this research and having people use the program, I will offer support over the Network. On the applicability of the theory of belief functions: All of the "problems" I've seen with applications of the theory of belief functions has come down to the following scenario. An expert system builder, usually without formal training in statistics, tries to apply the theory without making formal assumptions in the model. The informal assumptions he makes are incorrect, and the result is a paradox. This has always been a problem in statistics, and usually the first few weeks of any statistics course involves describing the pitfalls that careless assumptions produce. A statistician knows that she will need to carefully justify each assumption she makes (especially independence assumptions). The situation is no different in the more general belief function theory. Unfortunately, sloppy thinking leads to sloppy answers. This is true if the model you are using is first order logic, probability, belief function, fuzzy logic or the human brain. In order to get a good expert system using belief functions, one must apply the same model critisism/refinement procedures that one would apply to any other knowledge base. Work is only starting on those ideas for Bayesian and belief function approaches. I would be happy to discuss specific examples. --------------------- Russell Almond Dept. of Statistics, GN-22 U. of Washington Seattle, WA 98195 (206) 543-4302 almond@stat.washington.edu