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Author of FuzzyJess Talks About AI and Java : Page 2

Bob Orchard is the author of FuzzyJess, the Java-based, fuzzy logic API extension to Jess, and a veteran of many expert systems projects. In this interview, he discusses the state of artificial intelligence (AI), expert systems (ES), and the richness of possibilities for Java developers to utilize his tools for building fuzzy rule-based expert systems.

Figure 1. Bob Orchard is the Group Leader for the Integrated Reasoning Group at the National Research Council of Canada’s Institute for Information Technology.

Jason Morris: What drew you, a classically educated mathematician, to study fuzzy logic?

Robert Orchard: In the late-1980s, a visiting researcher from Poland with considerable expertise in fuzzy logic (Zenon Sosnowski), spent a couple of years in our lab. He added an initial set of extensions to CLIPS allowing fuzzy reasoning to be done. Just prior to leaving, he gave some talks on fuzzy logic and described the modifications to CLIPS. I started working with him, and when he left, I continued the development of FuzzyCLIPS, modifying the approach and adding many enhancements. We also developed a simple system to control the output of a shower with no knowledge—except the output temperature and flow. This ability to build a control system without a complex set of mathematical equations, using a set of simple English-like rules intrigued me.

JM: So, adding fuzzy logic to rule-based systems seems natural enough. What can you tell us about the original FuzzyCLIPS. Is it still used?

RO: Adding fuzziness to a rule based system like CLIPS certainly seemed natural to me. I don't think its impact on AI was huge, but I will claim that FuzzyCLIPS has had an impact. It has been extremely useful in training people about fuzzy rule-based systems since it has been used in many AI courses at universities and colleges. It has been the basis of many research projects (one paper in IEA/AIE 2004 references FuzzyCLIPS), and it has been used in a few commercial applications (some in the US). Since I started tracking about eight years ago, there have been more than 18,000 downloads and 3,000 of those were in the last year.

JM: According to Jess creator, Dr. Friedman-Hill, the original Jess project had to do with a novel approach to information protection and network intrusion detection. What was the impetus for FuzzyCLIPS and subsequently FuzzyJ and FuzzyJess? What were their intended purposes?

RO: We recognized that AI would play a major role in the future, and at the time the FuzzyCLIPS project started, we felt a need to be aware of the many facets of AI (machine learning, expert systems, natural language, etc.) including fuzzy logic. Initially we approached groups in the resource industries, trying to interest them in fuzzy logic for control, but they were not ready at the time to absorb this new and—to them—unproven, technology.

We then decided to release the software (FuzzyCLIPS) free for educational and research use, distributing it over the Internet. As our group grew, our expertise expanded in expert systems and machine learning, and the work on fuzzy systems lessened. Because of the strong interest in FuzzyCLIPS (indicated by large numbers of downloads), the growth of Java and the emergence of Jess, I was able to convince myself that a similar capability in Java was worthwhile. I designed the Java classes for FuzzyJ, and with the help of an excellent summer student, an implementation came about quite quickly. A short time later, FuzzyJess was added and the port from FuzzyCLIPS was completed. What we accomplished over this time was the distribution of a couple of tools that have proven useful for students, teachers, researchers and industry.

JM: How did the collaboration between you and Dr. Friedman-Hill begin on the FuzzyJess project?

RO: The collaboration began some time after I discovered Jess and noted how useful it would be to have a capability similar to FuzzyCLIPS in Jess. I monitored the Jess User Group for a while and noted how super the support from Ernest Friedman-Hill was. He seemed open to collaborations (encouraging such activities), so I approached him via email in mid-December 1998 to work on a way to integrate the FuzzyJ capabilities (not yet implemented) into Jess in a non-intrusive way. By June of 1999, we were working out the final details for the integration of FuzzyJ (now implemented) and Jess, and the alpha version of FuzzyJess was available in the summer of 1999 with a released version late in the fall of 1999. It was a very fulfilling collaboration, and I think we arrived at a very satisfactory solution. Later I was able to contribute part of a chapter to Ernest’s book, "Jess in Action" (2003, Manning Publications).

JM: Describe for the readers what types of problems lend themselves to using fuzzy logic instead of the more traditional Boolean logic.

RO: Much of the reasoning that humans do and the way we solve problems is closer to a fuzzy approach than a Boolean approach. The concepts we deal with on a day-to-day basis are most often fuzzy or imprecise (warm water, high pressure, strong winds) rather than crisp or precise (the light is on, the water temperature is greater than 212 degrees). So problems that inherently have some degree of imprecision or uncertainty in them can often be solved better using fuzzy logic than Boolean logic or even complex mathematical equations that try to model the uncertainty in the system about which we are reasoning.

Fuzzy logic has been applied in many areas, and its use is still growing. You'll find it in many control applications such as cameras, trains, anti-lock braking systems, elevators, cruise control and washing machines. You'll also find it used in decision making for financial systems (deciding who to give loans to and how much can be tolerated, etc.).

Fuzzy systems are often easier to design, build and maintain and can more easily reflect the human intuition/expertise than can be done with other modeling methods. Fuzzy systems can deal with imprecise and missing information, can model complex non-linear systems, can be written in natural language, are robust to changing situations, and they often out-perform mathematical systems and even humans in control and decision making. However, one would be wise to be careful to note when not to use fuzzy logic as well. (See the Mathworks Fuzzy Toolbox "Getting Started" documentation for a good explanation.)

JM: OK, but how is this different from using ordinary probability and statistics to reason with uncertainty?

RO: There is great controversy between the fuzzy camps and the probability camps over the relationship between probability theory and fuzzy sets. In my view of things, membership in a fuzzy set is not a probability in general.

Figure 2. A Typical Fuzzy Set: Fuzzy sets measure how much a value "belongs" to the set. In this case, as the fuzzy value approaches 0.5, you say that its membership approaches 100 percent.

Didier Dubois and Henri Prade are two of the pioneers of the theory and application of fuzzy systems. They wrote an article that dealt with this controversy ("Fuzzy Sets And Probability: Misunderstanding, Bridges And Gaps, 1993, Proceedings of the Second IEEE Conference on Fuzzy Systems"). Others have stated that probability deals with the likelihood that something has a particular property, whereas fuzzy logic deals with the degree to which the property is present.

JM: With what additional technologies or academic fields should Java programmers be familiar before attempting to use FuzzyJess?

RO: Java programmers ought to become familiar with rule-based systems and at least the rudiments of fuzzy logic before attempting to use FuzzyJess. It can take some time for programmers, used to a language like Java, to understand the operation of a rule-based system like Jess. Jess in Action can help with both of these, but some primers on fuzzy logic might also be required.

JM: What is the difference between Fuzzy Jess and Jess proper? How does FuzzyJess work?

RO: FuzzyJess is just Jess with some extra capabilities for handling rules with fuzzy pattern slots and that assert facts with fuzzy slot values. However, one has to learn the language of fuzzy systems as implemented in FuzzyJ/FuzzyJess. This includes: FuzzyVariables and FuzzySets that allow you to define the concepts that you will be describing, like temperature with the terms hot, cold, warm, and cool; FuzzyValues that are specific instances of the fuzzy concepts, like cold or hot temperature; and FuzzyRules.

So, a non-fuzzy Jess rule might look something like this:

Author's Note: If you are wondering what this code is, because it certainly isn't Java, it is Jess's internal scripting language also referred to as "Jess." Its syntax is similar to CLIPS, but it differs in functionality and implementation since it primarily serves to expose Jess's Java API in an interpreted, command-line way. In other words, you can program in Jess (and hence FuzzyJess) by writing Jess batch files, by programming its Java API directly, or by a mixture of both! This flexibility is one of the hallmarks of using Jess.

The fuzzy-match function compares a fuzzy value in the fact slot (?t) to a fuzzy value defined in the 2nd parameter of the fuzzy-match function ("hot" temperature). If there is a match, the rule fires and an increase-fan-speed fact is asserted with a fuzzy slot describing the change—in this case the change of fan speed will be "large."

You need to understand how the fuzzy outputs from the rules are combined automatically and how to defuzzify (convert from a fuzzy to a crisp value) the final fuzzy output values. Of course there are plenty of details to attend to, but this is described in the documentation for the FuzzyJ Toolkit and FuzzyJess as well as in the book, "Jess in Action," by Ernest Friedman-Hill (2003, Manning Publications).

JM: Now, rule portability and system scalability are major issues in constructing enterprise-class, rule-based expert systems. Can FuzzyJess rules be constructed in other formats like XML, RuleML, or DAML?

RO: Because we don't change the syntax or semantics of Jess in any way, the things that can be done with Jess can be done with FuzzyJess. Something like RuleML is crippled because it tries to meet the needs of all rule based systems. Therefore, the rule systems one can express in RuleML are a very limited set of what can be expressed in Jess. You might be able to represent a FuzzyJess rule system in RuleML, but it wouldn't execute on any runtime rule engine except Jess with the FuzzyJess additions. RuleML will likely never allow one to define fuzzy rules.

JM: What can you mention about the companies have licensed FuzzyJess and how they are using it in industry?

RO: As a federal research lab and not a commercial organization, we are not putting a lot of effort into selling FuzzyJ/FuzzyJess on a mass-market basis. However, we do require that commercial users of FuzzyJ/FuzzyJess purchase a license. I am not able to reveal the names of the companies (there are only a handful), and they have not told us what products they have marketed or how they are using to toolkit. I can tell you that all of the companies are US companies. Perhaps this says that US companies are some of the most honest in the world in honoring on-line agreements.

JM: The next major release of Jess (version 7.0 code named Charlemagne) will be an Eclipse plug-in. Will there be any noticeable changes or upgrades in FuzzyJess (like a fuzzy rules editor) to coincide with its release?

RO: FuzzyRules are just like Jess rules, so the rule editor for Jess will handle fuzzy rules by default. However, what many other commercial systems have that is quite nice is an interface for defining fuzzy sets and terms and even the fuzzy rules graphically. This won't likely happen for FuzzyJess in the near future. Unless there are specific user requests, I expect that for the next while there will only be minor updates to the FuzzyJ/FuzzyJess Toolkit (the users appear to be satisfied with the current functionality).

JM: What features of FuzzyJess make it a better choice for programming fuzzy rules than something like FIDE from Aptronix or the MATLAB Fuzzy Logic Toolbox from the Mathworks?

RO: Well, both FIDE and the MATLAB Fuzzy Logic Toolbox are excellent. FIDE in particular provides a nice graphical environment for creating fuzzy systems and testing them. However, FuzzyJ/FuzzyJess is, I believe, more flexible since it is a Java API. It can be used in any Java program, and when used with Jess, it gains the full flexibility of the Jess rule-based environment (fuzzy and non-fuzzy reasoning with a rich pattern matching capability in the rules). It is relatively simple for end-users to extend due to the strong Java/object connection. For example, one can easily add one's own fuzzy set types or defuzzification methods or even change some aspects of the way the fuzzy rules combine their results.