Hayes-Roth, F., Waterman, D., and Lenat, D. (Eds) (1983). Building Expert Systems. Addison-Wesley.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a human expert would summarise time series data. 3. Knowledge Acquisition The first year of SUMTIME has focused, after an initial literature review, on trying to understand how human experts summarise time series data. The main objectives of the Knowledge Acquisition (KA) activities have been [3, 11]: 1. To determine the task model of summarisation 2. To determine the types of knowledge required for the task model. 3. To acquire all the required types of knowledge in detail. The KA activities in the two domains (meteorology gas turbine) have been different because the two domains differ ....

Hayes-Roth, F., Waterman, D., and Lenat, D. (Eds) (1983). Building Expert Systems. Addison-Wesley.

....in order to assist designers, developers and users is advocated by many reference and text books on expert systems. For instance, the use of metarules (rules containing knowledge about the organization of the knowledge base) as a means of navigating among the various rule groups is mentioned in [11, 17]. Several papers [8, 22, 26] address explicitly the issue of the knowledge base partitioning in production systems from the perspective of facilitating their verification, validation and maintenance. In [1] the authors have discussed a method for clustering Prolog rules and facts for distributed ....

F. Hayes-Roth, D.A. Waterman, and D.B. Lenat. Building Expert Systems. Addison-Wesley, Reading, MA, 1983.

.... principles for modifying these attitudes, operators for adding mental attitudes, the cognitive frame, the language, control procedures, social interaction propensities, and principles and mechanisms for generating inter agent social behaviour (Carley 1989; Davis and Lenat 1980; Dennet 1987; Hayes Roth et al. 1983; Waterman 1986) The cognitive structure changes as the agent interacts with its physical and social environment and acquires new information. This, in turn, affects the agent s behaviour and its interaction with the environment (Carley 1989) The two main components of each negotiating agent s ....

Hayes-Roth, F., D. A. Waterman, and D. R. Lenat (eds.). (1983). Building Expert Systems, Reading, MA: Addison-Wesley.

....problems, that they do not answer a phone on someone else s desk, etc. Models can be built at di#erent levels to describe di#erent phenomena. But the constructs in a modeling language often reflect assumptions about what a model should include. Specifically, the original paradigm of expert systems (Hayes Roth, Waterman Lenat, 1983) assumes that knowledge is exclusively technical, objective and used for reasoning. In the original formulation, all human action is viewed as being problem solving in an unspecified environment. At issue is not so much the modeling apparatus (though indeed more is needed to model human attention ....

HAYES-ROTH, F., WATERMAN,D.&LENAT, D., eds. (1983). Building Expert Systems. New York: Addison-Wesley.

....an expert s knowledge of a complex document. But Thinksheet takes a simpler approach that avoids some of the common pitfalls of expert systems. A common problem with expert systems is that a new application of an existing expert system often requires that a whole new expert system be written [HRWL84, Ign91]. The main reason is that there is no easy separation in such a system between (its often very powerful) rules and data. Furthermore, expert systems have the added complexity that they are state driven sometimes inputting the same values twice will not result in the same output. They also often ....

F. Hayes-Roth, D. Waterman, and D. Lenat. Building Expert Systems. Addison-Wesley, 1984.

....requires a more fine grained analysis of domain and task characteristics. This is nowhere more true than for diagnostic systems. Since the MYCIN experiments (Shortliffe [8] many different heuristic, model based, and hybrid approaches have been reported. For review articles see (Hayes Roth [3]) and relevant chapters of (Shrobe [9] In order to understand how domain and task characteristics motivate particular system functions and architectures, this paper discusses TEST . Although this analysis is no substitute for a comprehensive analysis of diagnostic techniques, it does provide a ....

Hayes-Roth, F., Waterman, D. A., and Lenat, D. B., Building Expert Systems, Addison-Wesley, Reading, Mass. (1983).

....problem domain. Very often these shells are programmed in programming languages where logic deduction can easily be represented, such as LISP or PROLOG, but standard programming languages such as C and Pascal can of course be used. Rule based expert system can be categorized into several areas (Hayes Roth et al. 1996). Referring to Figure 6 they comprise the planning, control and analysis phase of the production cycle. In developing rule based expert systems two players are essential, of course the expert, but in addition the so called knowledge engineer. The role of the knowledge engineer is to extract ....

Hayes-Roth, F., D.A. Waterman & D.B. Lenat. 1996. Building Expert Systems, Addison-Wesley.

....such a theory. The best known language for a formal model is first order logic which expresses facts and rules in a single, formalized matter [Gallaire 1984b] and derives knowledge by using formal rules. The deductive power of logic inference systems is typically used in AI systems (Barr 1982] [Hayes Roth 1983]. Geographic Information Systems need these methods to help integrate data from different sources into a unified system [Robinson 1987a] A deficiency of any AI based system is the quantitative difference between AI expert systems and database management systems [Mylopoulos 1981] while database ....

F. Hayes-Roth and others. Building Expert Systems. Addison-Wesley Publishing Company, Reading, MA, 1983.

....Knowledge acquisition (KA) is the transfer and transformation of expertise from some knowledge source to some explicit knowledge representation usually denoted as knowledge base that enables the effective use of the knowledge. This definition is based on the one byHayes Roth et al. from 1983 [3]. It has been generalised slightly to meet the application of knowledge acquisition in experimental software engineering as addressed in the remainder of this paper. A KA method is an organised approachtoknowledge acquisition. It involves a defined process and guidelines for process execution. A ....

FrederickHayes-Roth, Donald A. Waterman, and Douglas B. Lenat, editors. Building Expert Systems. Addison-Wesley, 1983.

....is often inadequate for problem solving; thus the knowledge engineer and expert must work together to extend and refine it. One of the most difficult aspects of the knowledge engineer s task is helping the expert to structure the domain knowledge, to identify and formalize the domain concepts. (Hayes Roth, Waterman Lenat, 1983) Thus, the basic model for knowledge engineering has been that the knowledge engineer mediates between the expert and knowledge base, eliciting knowledge from the expert, encoding it for the knowledge base, and refining it in collaboration with the expert to achieve acceptable performance. Figure ....

Hayes-Roth, F., Waterman, D.A. & Lenat, D.B., Eds. (1983). Building Expert Systems. Reading, Massachusetts: Addison-Wesley.

....While Protg began as a small application designed for a medical domain (protocol based therapy planning) it has grown and evolved to become a much more general purpose set of tools for building knowledge based systems. The original goal of Protg was to reduce the knowledge acquisition bottleneck (Hayes Roth et al., 1983) by minimizing the role of the knowledge engineer in constructing knowledge bases. In order to do this, Musen (1988, 1989b) posited that knowledge acquisition proceeds in welldefined stages and that knowledge acquired in one stage could be used to generate and customize knowledge acquisition tools ....

....Intelligence. Expert systems research had produced some stunning successes (see, for example, Buchanan and Shortliffe, 1984) McDermott , 1980) and (Bachant and McDermott, 1984) To many people in the field it seemed that AI was on the verge of a dramatic breakthrough. Perhaps the authors of (Hayes Roth et al., 1983) put it best when they wrote: Over time, the knowledge engineering field will have an impact on all areas of human activity where knowledge provides the power for solving important problems. We can foresee two beneficial effects. The first and most obvious will be the development of knowledge ....

Hayes-Roth, F., Waterman, D., and Lenat, D. (Eds) (1983). Building Expert Systems. AddisonWesley.

....in building intelligent systems is how to represent knowledge, that is, how to structure knowledge in a way that facilitate common types of inferences to be made. For research and practice in knowledge based systems, some types of logic or rule based frameworks are usually adopted ( 44] and [13]) However, they are far away from matching the capacity and exibility of human reasoning (see [4] and [39] What is the problem Knowledge is hard to grasp, as discovered by many leading researchers (cf. Minsky [21] and Hayes Roth et al. [13] notwithstanding the fact that small chunks of it ....

.... or rule based frameworks are usually adopted ( 44] and [13] However, they are far away from matching the capacity and exibility of human reasoning (see [4] and [39] What is the problem Knowledge is hard to grasp, as discovered by many leading researchers (cf. Minsky [21] and Hayes Roth et al. [13]) notwithstanding the fact that small chunks of it for a narrowly defined domain can be extracted and structured into rule based, frame based, or other similar systems. Psychological experiments reveal that in human cognition various kinds of knowledge exist and they are used in di erent ways ....

[Article contains additional citation context not shown here]

F. Hayes-Roth, D.A. Waterman and D.B. Lenat, eds. Building Expert Systems, AddisonWesley, Reading, MA. 1983

.... base from the problem solvers that operate on it is reminiscent of the situation in firstgeneration expert systems in which developers made a distinction between the knowledge base (typically a collection of rules or frames) and the inference engine that would operate on that knowledge base (Waterman et al. 1983). In first generation expert systems, however, the knowledge base did not consist of an explicit domain model that could serve as input to a variety of problem solvers. Instead, the knowledge base was always a set of representations that were dependent on one specific inference engine (e.g. the ....

Waterman, D.A., Hayes-Roth, F., and Lenat, D.B. (eds). (1983). Building Expert Systems. Reading, MA: Addison--Wesley.

....for data mining [1] This section will describe expert system and data mining technologies and how they are evolving to solve complex industrial problems. 1. 1 Expert Systems Expert systems are programs which represent and apply factual knowledge of specific areas of expertise to solve problems [2]. Expert systems have been applied extensively within the telecommunications industry, but not without problems. Early expert systems required a knowledge engineer to acquire knowledge from the domain experts and encode this knowledge in a rule based expert system. These rules were very ad hoc ....

Hayes-Roth, F., Waterman, D., Lenat, D. eds. (1983), Building Expert Systems, Addison-Wesley, Reading. MA.

....To provide knowledge level specifications, formal methodologies for developing KBSs were created. With these methodologies, it is possible to specify a KBS with hierarchical levels of abstractions, providing modularity, ease of understanding, modifiability and reusability. In earliers KBSs [12], facts are combined with their use. Clancey showed [5] that the separation of domain knowledge from control knowledge makes the KBSs easier to understand, to maintain and to give explanations. Domain knowledge is the set of concepts and their relations that corresponds to the conceptualization of ....

F. Hayes-Roth, D. Waterman, and D. Lenat. Building Expert Systems. Addison Wesley, 1983.

....applications that we had solved previously [26] We found that the rules we used in these applications were quite complex. They involved, in general, multiple entities, Boolean operators, functions (max, avg, etc. set operators ( member in ) and others. Simple IF THEN rules (of the MYCIN type [5]) were not sufficient. We realized that the closest analogy in capabilities to our requirements can be found in Database query languages such as SQL [13] and specifically the TRAPS syntax for Rules is quite similar to a typical syntax of a database query language. Although, a language such as SQL ....

Hayes-Roth F. et. al. (ed.), Building Expert Systems, Addison-Wesley Inc., London, 1983.

....standard requirements analysis in software engineering involves end users; similarly, knowledge acquisition in knowledge engineering involves experts. Still knowledge acquisition has often been referred to as bottleneck in the business of designing and applying expert systems to real problems (Hayes Roth et al. 1983). In addition, the involvement of real users and experts is often neglected in later stages of the development, resulting in systems which are not very usable. In this paper we describe the life cycle of a system that has standard features of an ITS, but is also useful in real classrooms. We ....

Hayes-Roth, F., Waterman, D. A. & Lenat, D. (1983). Building Expert Systems, Reading, Mass.: AddisonWesley.

....in the rule and those already represented in the knowledge base. However, the dependence upon interaction with the expert remained a bottleneck in the knowledge acquisition process. Much work in machine learning grew out of the desire to further automate the building of knowledge bases. 1 From [Building Expert Systems 83] 4 1.2.2. Similarity Based and Explanation Based Learning There are two disparate tasks that bear on knowledge acquisition. The first is the task of automating the acquisition of new knowledge. The second involves improving performance through the reformulation or reorganization of currently ....

F. Hayes-Roth, D. A. Waterman, and D. B. Lenat, eds. Building Expert Systems. Addison-Wesley, Reading, Massachusetts, 1983.

....to help solving the route finding problem. We believe that one of the reasons is that Dijkstra algorithm, A and others are already relatively efficient for the task. In the past, knowledge based approach has been mostly applied for solving problems that do not have efficient algorithmic solutions [Hayes Roth et al. 1983; Winston, 1992] However, from our experiences, we can say that although Dijkstra algorithm (or A ) is efficient, knowledge based approach can be successfully incorporated to produce even more efficient solution methods. Our knowledge based route finding can be described as using knowledge about ....

....in real situations it is not necessary to search through the whole network in order to find the solution. They may produce solutions unsuitable for human users as they may use too many minor roads, which is against the human preference of traveling on major roads. Knowledge based problem solving [Hayes Roth et al. 1983; Winston, 1992] emphasizes the use of human problem solving strategies on a computer. This technology has been used in many applications. However, for route finding, using knowledge based approach alone will not be efficient. After applying human knowledge about the network, a search is still ....

F. Hayes-Roth, D.A. Waterman, and D.B. Lenat (eds), Building Expert Systems, AddisonWesley, 1983.

....in building intelligent systems is how to represent knowledge, that is, how to structure knowledge in a way that facilitate common types of inferences to be made. For research and practice in knowledge based systems, some types of logic or rule based frameworks are usually adopted ( 44] and [13]) However, they are far away from matching the capacity and exibility of human reasoning (see [4] and [39] What is the problem Knowledge is hard to grasp, as discovered by many leading researchers (cf. Minsky [21] and Hayes Roth et al. [13] notwithstanding the fact that small chunks of it ....

.... or rule based frameworks are usually adopted ( 44] and [13] However, they are far away from matching the capacity and exibility of human reasoning (see [4] and [39] What is the problem Knowledge is hard to grasp, as discovered by many leading researchers (cf. Minsky [21] and Hayes Roth et al. [13]) notwithstanding the fact that small chunks of it for a narrowly defined domain can be extracted and structured into rule based, frame based, or other similar systems. Psychological experiments reveal that in human cognition various kinds of knowledge exist and they are used in di erent ways ....

[Article contains additional citation context not shown here]

F. Hayes-Roth, D.A. Waterman and D.B. Lenat, eds. Building Expert Systems, AddisonWesley, Reading, MA. 1983

....with like interests. Q A builds on previous techniques for building question driven information retrieval systems, but represents a significant step forward in the flexibility it provides for the organization of knowledge, as well as its user interface. NOTES 1. This phrase was first used by (Hayes Roth et al. 1983) in reference to the time intensive effort of constructing rule based expert systems. We think it is apt here as well, even though this research is primarily concerned with the capture of textual representations. 2. It is well beyond the scope of this paper to discuss them all. 3. Q A was first ....

Hayes-Roth, F., Waterman, D., & Lenat, D. (Eds.). (1983) Building Expert Systems. Reading, MA: Addison-Wesley.

.... derive a complete model of the underlying universe of discourse (also referred to as the application domain, or problem domain) Extracting this information from the universe of discourse is a hard problem, and is comparable to the problem of knowledge elicitation for the design of expert systems [58, 9, 34]. A wide range of modern conceptual modelling techniques start out by verbalising sample forms, cases, etc. taken from the universe of discourse. This verbalisation process is usually conducted in close cooperation with a domain expert. Examples of such approaches are the ER variation discussed in ....

F. Hayes-Roth, D.A. Waterman, and D.B. Lenat. Building Expert Systems. Addison-Wesley, Reading, Massachusetts (1983).

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Intelligence Hayes-Roth, 1983. Building Expert Systems.

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F. Hayes-Roth, D.A. Waterman. amd D. Lenat, (eds.), Building Expert Systems, Mass.: Addison-Wesley, 1983.

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Hayes-Roth F, Waterman D, and Lenat D. Building Expert Systems. Reading,MA: Addison-Wesley. 1983; 12

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