R. Davis en R.G. Smith, Artificial Intelligence, 20:63-109, 1983.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....quantity, and their behavior is determined by some measure of aggregation (such as a sum, product, or average) over the scalars of different agents. c) Negotiation: As in voting, negotiating agents exchange a series of messages before a decision is made. The canonical example is the Contract Net [1] (see also TNO report DIS RPT 000005 78 of 82 paragraph 3.2.3.1) Voting and negotiation differ in three ways. First, while both a bid and a vote are intended to influence the behavior of the recipient, the bid has the additional purpose of acquiring further assignments for the sender. Second, ....

R. Davis en R.G. Smith, Artificial Intelligence, 20:63-109, 1983.

....our example, the requirement x (x observables) cannot be derived from the domain knowledge because it is concerned with the input. However, assuming an input for deriving a diagnosis is not a critical assumption. For example, a more serious assumption would be the single fault assumption (cf. [8]) Formulating it as a requirement on domain knowledge enforces that each possible fault combination is represented as a single fault by the domain knowledge. Therefore, it is often used as an assumption that limits the scope of the problems that can be handled correctly by the system. Cases, ....

R. Davis: Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence, 24: 347---410, 1984.

....assumption. 4 It remains to ensure that Init delivers a correct set of hypotheses. An easy way to achieve this is to deliver the entire set of hypotheses (given the monotony of the problem) i.e. h Init. 4. For example, a more serious assumption would be the single fault assumption (cf. [Davis, 1984]) Formulating it as a requirement on domain knowledge enforces that each possible fault combination is represented as a single fault by the domain knowledge. Therefore, it is often used as an assumption that limits the scope of the problems that can be handled correctly by the system. Cases where ....

R. Davis: Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence, 24: 347---410, 1984.

....restore the consistency of the database. 1 Introduction During the last decade model based diagnosis has become a prominent research area for describing techniques that can be used to identify malfunctioning components of a system. Based on early work in model based diagnosis systems [dK76, Dav84, Gen84] several proposals and contributions have been made in the following years, e.g. Rei87, dKW87] for different technical domains, in particular electronic circuits. 1 Extensions on diagnosis have been made in, e.g. CR91, FN92, FGN94] to diagnosis repair systems. These systems use the ....

....consists of locating those components in the system whose abnormal behavior causes an observed misbehavior of the system. In model based diagnosis there is a widespread use of constraint like descriptions [SS80] for the structure and behavior of systems. Many approaches like, e.g. Gen84, Dav84] 1 For an extensive overview of model based diagnosis see [HCdK92] which comprises seminal and most referenced papers of this research area. and [Rei87] successfully exploit such descriptions for diagnosing malfunctioning systems and components. The basic idea is to model the behavior of ....

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R. Davis: Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence 24:3 (December 1984), 347--410. Also in [HCdK92].

....interested in problemsolving in principle but in practice, further assumptions have to be introduced that either decrease the worst case, or at least the average case behavior. A drastic way to reduce the complexity of the diagnostic task is achieved by the single fault or N fault assumption (SFA) [Davis, 1984], which reduces the complexity to polynomial in the number of components. If the single fault assumption holds, the incorrect behavior of the device is completely explainable by one failing component. Interestingly, the same assumption can either be interpreted as a requirement on domain knowledge ....

....possible fault has to be represented as a single entity. In principle this causes complexity problems for the domain knowledge as each fault combination (combination of faulty components) has to be represented. However, additional domain knowledge could be used to restrict the exponential growth. [Davis, 1984] discusses an example of a representation change where a 4 fault case (i.e. 15 different combinations of faults) is transformed into a single fault. A chip with four ports can cause faults on each port. When we know that the individual ports never fail, but only the chip as a whole, a fault on ....

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R. Davis: Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence, 24: 347-410, 1984.

....know and how they reason, in intelligent systems [3] Many AI systems have used descriptions of their own problem solving for several different tasks. These descriptions have taken a variety of forms depending on the view they adopt for problem solving and on the task they are used for. Teiresias [7, 8], for example, views problem solving as recursive rule activation. It models its rule base in terms of meta rules that describe which rules can be used as evidence for or against inferences on domain objects. Teiresias uses its meta rules to guide the domain expert in identifying erroneous rules ....

R. Davis: Meta-Rules: Reasoning about Control. Artificial Intelligence 15:179-222 (1980)

....behaviour of the device using its model. Because the reasoning part is represented separately from domain knowledge, it can be reused for different domains. This paradigm of model based diagnosis gave rise to the development of general approaches to diagnosis, such as constraint suspension [Davis, 1984], DART [Genesereth, 1984] GDE [de Kleer Williams, 1987] and several extensions to GDE (GDE [Struss Dressler, 1989] Sherlock [de Kleer Williams, 1989] In this paper, we will focus on assumptions underlying approaches to diagnostic problem solving. In Section 2, we discuss assumptions ....

.... artefact has a defect [Davis Hamscher, 1988] These assumptions are also necessary for the meta level decision whether a diagnosis problem is given at all (i.e. whether there is an abnormality in system behaviour) This decision relies on a further assumption: the no design error assumption [Davis, 1984] which says that if no fault occurs, then the device must be able to achieve the desired behaviour. In other words, the discrepancy must be the result of a fault situation where some parts of the system are defect. It cannot be the result of a situation where the system works correctly, but cannot ....

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R. Davis: Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence, 24: 347-410, 1984.

....assumptions. This gives rise to the question Which completion should we use (probably depending on the application) 2 In this paper, a general framework is proposed for studying database completions. We characterize those completions which can be represented by means of preference relations [Dav80, McC80, Sho87] and investigate their relation to the class of closed world assumptions [Rei78, Min82, BL89] as well as to two versions of the default logic: simple defaults with constraints [Poo88] and normal defaults [Rei80, Luk85] This is of course only the beginning, and the goal at the moment is mainly ....

....OE fl 2 to denote that we prefer fl 1 to fl 2 and not vice versa. Predicate circumscription, for instance, uses the preference relation defined by fl 1 OE fl 2 : fl 1 [p] fl 2 [p] for all predicates p (ae for at least one) The use of preference relations to define completions originates from [Dav80], completions defined by general preference relations have been investigated in [Sho87] Definition 3.1.1 (Minimal Models) Let OE be a partial order relation on the Herbrand structures S Sigma . Then the following modeltheoretic completion min OE is given by OE: min OE ( Gamma) ffl 2 ....

M. Davis: The mathematics of non-monotonic reasoning. Artificial Intelligence 13 (1980), 73--80. 15

....restore the consistency of the database. 1 Introduction During the last decade model based diagnosis has become a prominent research area for describing techniques that can be used to identify malfunctioning components of a system. Based on early work in model based diagnosis systems [ dK76; Dav84; Gen84 ] several proposals and contributions have been made in the following years, e.g. Rei87; dKW87 ] for different technical domains, in particular electronic circuits. 1 Extensions on diagnosis have been made in, e.g. CR91; FGN94 ] to diagnosis repair systems. These systems use ....

....consists of locating those components in the system whose abnormal behavior causes an observed misbehavior of the system. In model based diagnosis there is a widespread use of constraint like descriptions [ SS80 ] for the structure and behavior of systems. Many approaches like, e.g. Gen84; Dav84 ] and [ Rei87 ] successfully exploit such descriptions for diagnosing malfunctioning systems and components. The basic idea is to model the behavior of each component as a set of constraints, z This work was partially supported by the Volkswagen Stiftung, and by ESPRIT BRA ModelAge (WG ....

R. Davis: Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence 24:3 (December 1984), 347--410.

....for controlling the parallel execution strategy. The programmer needs only to combine the directives in the meta program to implement a different search algorithm over a state space, leaving the logic program untouched. Reasoning about control in the form of meta rules initially appeared in [9], achieving pruning of the search space. In logic programming, the importance of control over logic programs has been demonstrated, either by altering the left to right execution of subgoals of the body of a clause [10] or by altering the ordering of program clauses if certain conditions are ....

R. Davis: Meta-rules: Reasoning about Control, Artificial Intelligence, 15:3, 179222 (1980).

....France THE JOURNAL OF LOGIC PROGRAMMING c fl Elsevier Science Inc. 1994 655 Avenue of the Americas, New York, NY 10010 0743 1066 94 7. 00 2 CLP(BNR) 2] Interlog [17, 6, 20] Prolog IV [5] All these systems use an arc consistency like algorithm [25] adapted for numeric constraints [9, 8]. This standard interval narrowing algorithm (named algorithm IN in the following) has two main drawbacks: ffl the existence of slow convergences , leading to unacceptable response times for certain constraint systems; ffl the early quiescence [9] i.e. the algorithm stops before reaching ....

.... [25] adapted for numeric constraints [9, 8] This standard interval narrowing algorithm (named algorithm IN in the following) has two main drawbacks: ffl the existence of slow convergences , leading to unacceptable response times for certain constraint systems; ffl the early quiescence [9], i.e. the algorithm stops before reaching a good approximation of the set of possible values. The focus of this paper is on the first problem. It shows that there is a strong connection between the existence of cyclic phenomena and slow convergence. The main goal is to dynamically identify ....

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E. Davis Constraint propagation with interval labels. Artificial Intelligence, 32, 281--331, (1987).

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R. Davis: Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence, 24: 347-410, 1984.

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