This demo presents RecAdvisor, a prototype recommender system for finding and recommending potential Ph.D. supervisors for students by identifying different criteria to consider when selecting a supervisor.

A dynamic reasoning system (DRS) is an adaptation of a conventional formal logical system that explicitly portrays reasoning as a temporal activity, with each extralogical input to the system and each inference rule application being viewed as occurring at a distinct timestep. Every DRS incorporates some well-defined logic together with a controller that serves to guide the reasoning process in response to user inputs. Logics are generic, whereas controllers are application specific. Every controller does, nonetheless, provide an algorithm for nonmonotonic belief revision. The general notion of a DRS comprises a framework within which one can formulate the logic and algorithms for a given application and prove that the algorithms are correct, that is, that they serve to (1) derive all salient information and (2) preserve the consistency of the belief set. This article illustrates the idea with ordinary first-order predicate calculus, suitably modified for the present purpose, and two examples. The latter example revisits some classic nonmonotonic reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be resolved in the context of a DRS, using an expanded version of first-order logic that incorporates typed predicate symbols. All concepts are rigorously defined and effectively computable, thereby providing the foundation for a future software implementation.

A previous publication introduced the logic Q for qualified syllogisms, which formalizes arguments such as “Most birds can fly; Tweety is a bird; therefore it is likely that Tweety can fly”. This provided a probabilistic semantics for fuzzy quantifiers (most, many, few, etc.), fuzzy usuality modifiers (usually, often, seldom, etc.), and fuzzy likelihood modifiers (likely, uncertain, unlikely, etc.). That work dealt only with crisp (nonfuzzy) predicates like Bird and CanFly, however. The present work proposes a simple and intuitively natural way to expand the semantics of Q so as to accommodate syllogisms such as “Most Swedes are tall; Helge is a Swede; therefore it is likely that Helge is tall”, where tall is a fuzzy predicate. To accomplish this, a formulation of the notion of the probability of a fuzzy event is proposed. It is shown that the new semantics validates the intended syllogisms as well as numerous other propositions and rules, including a rendition of the axioms and inference rules of classical first-order predicate calculus. These results may be viewed as steps toward future applications. A future work will follow the lines of the previous work and show how Q thus extended may be employed for nonmonotonic reasoning with fuzzy predicates.a

The newly envisioned semantic Web contains as a component what may be called a Web of Trust, consisting of the trust relationships held between its participants. In terms of this, the degree to which participant A believes what is believed by participant B depends on the degree to which A trusts B. This paper presents a formal language with well-defined semantics within which such a participant (or agent) can express the relevant conditions of belief and trust, and outlines some key techniques for reasoning with these expressions. Novel in this treatment is the use of linguistic, rather than numeric, measures of belief. The aim in this is to make the language and reasoning system more intuitive for the human user. Also novel is an explicit delineation of the context within which the language and reasoning techniques are to be applied

This paper discusses recent applications of fuzzy sets and the theory of approximate reasoning. The primary focus is on fuzzy logic control (FLC). We begin with a brief history of the key ideas, a survey of recent applications, and a discussion of the genesis of FLC in Japan. We then turn to a study of the general principles of FLC, considering it as a combination of ideas from conventional control theory, artificial intelligence, and fuzzy sets theory. We next provide a detailed analysis of a simple application in consumer electronics, namely, a fuzzy washing machine developed by Hitachi Corporation. In concluding sections we briefly consider other types of applications, including recent work on pilotless helicopters, fuzzy expert systems, and the concept of a fuzzy computer, and we discuss the potential for future developments. It is our opinion that the subject of FLC is still very much in its infancy, and that recent events mark the beginning of an entirely new genre of "intelligent" control.>