Book Reviews AI Magazine Volume 12 Number 1 (1991) (© AAAI) BookReviews

Artificial Intelligence, Numeric is used for what- applied in modeling and simulation Simulation, and Modeling if questions, but diagnosis and expla- as the quantitative approach (for nation by symbolic reasoning are example, minimizing an objective Mark E. Lacy used for why questions. Execution on function that describes how well a computing machinery can be differ- model fits the available data). Typi- As a scientist doing modeling ent as well: AI tools do not lend cally, the challenging us are and simulation, I have been interested themselves to the advantages of par- complex, and we are lucky if we can for some time in ways that modeling allel architectures as well as mathe- make any qualitative statements and simulation and AI could be of value matical tools do, and systems that about the behavior of a system other to each other. After all, both areas have attempt to integrate AI and mathe- than, perhaps, statements regarding their roots in putting knowledge into matical approaches pose a real chal- stability or long-term steady-state useful representations. I have specu- lenge to the development of fast behavior. Only the simplest of systems lated (AI Magazine, summer 1989, pp. software and hardware. can be analyzed in terms of its quali- 4348) that the scientist of the future, There is great potential, however, tative behavior. This reason is one of in applying computing to his(her) for AI and simulation to take advantage the main ones for performing simu- work, could benefit from a virtual of each other’s strengths. Because lation: Intuitive analysis can seldom laboratory environment that pro- modeling and simulation is a com- answer the important questions vides an integration of mathematical plex undertaking, requiring expertise about complex systems. We tend to and statistical tools with AI methods in several fields, AI tools might be obtain quantitative impressions of a to assist in modeling and simulation. able to help the “simulationist” system first, then use these impressions One learns from reading Artificial better handle the complexity of the to infer qualitative aspects of system Intelligence, Simulation, and Modeling modeling and simulation process, behavior. Therefore, the introduction (‘John Wiley and Sons, New York, 1989, leading him(her) through the itera- of tools that assist in the qualitative 556 pages, $44.95) that a significant tive cycle of devising and testing new analysis of dynamic systems will amount of research relating to the models. Likewise, AI systems might greatly benefit the modeling commu- integration of AI and simulation is be able to use modeling and simula- nity. Currently, applying the qualita- under way. This integration will not tion to arrive at decisions for com- tive reasoning approach even to only help those doing modeling and plex systems where simple heuristics simple systems is not for the faint- simulation go beyond what currently are not appropriate or feasible. hearted, as these chapters demon- available tools allow, it will also give Artificial Intelligence, Simulation, strate. Major advances are necessary those developing knowledge-based and Modeling includes many chapters to make the tools more easily and systems the opportunity to draw on on these concepts and others, includ- widely applicable. the advantages of modeling and sim- ing the use of qualitative reasoning Do the research projects described ulation to arrive at some conclusions. and qualitative simulation, the exe- in this book meet the needs of end Editors Lawrence E. Widman, Ken- cution of integrated AI-simulation users? From several of the descriptions neth A. Loparo, and Norman R. systems on machines of advanced of the projects under way, it was not Nielsen succeeded in providing us architecture, formalisms and languages clear to me whether the teams that with a broad view of current research for integrated systems, and specific are tackling these projects include along these lines. examples of systems under develop- domain experts or whether opinions The many authors contributing to ment. My focus here, biased by my from domain experts are being this book explore, on several levels, work in modeling and simulation, is solicited. I presume that it will often the relationships between AI and sim- on the work described on qualitative be a domain expert who wishes to ulation and what challenges and reasoning and simulation. use an integrated AI-simulation promises these relationships suggest. One of the key challenges to inte- system to help him(her) at work. On the one hand, certain similarities grating AI and simulation is the suc- Some kind of evaluation by domain stand out: Both AI and modeling and cessful integration of qualitative and experts is a must; otherwise, much simulation are concerned with the quantitative information. Several time and effort can be wasted in representation of an external reality. chapters concern qualitative reasoning inventing the wrong wheel. End Predicting an outcome using simula- and qualitative modeling and simula- users should have input from the tion is analogous to forward chain- tion, including the use of mathemati- beginning and be given frequent ing, and mathematical optimization cally based models by qualitative opportunities to provide direction to is analogous to backward chaining. reasoning in AI systems and the use an AI-simulation integration team as On the other hand, AI and simulation of qualitative approaches in a mod- a project progresses. However, this are concerned with representations eling situation. The qualitative book does express some caution that are used in different ways. approach is not as commonly about the use of powerful integrated

100 AI MAGAZINE Book Ret iews systems by naive users. W.; Maclay, J.; and Achs, M. J 1987. in part result from the availability in With respect to breadth of coverage Modeling and Artificial Intelligence Great Britain of fewer of the expert and potential readership, Artificial Approaches to Enzyme Systems. Federa- system shell products that are com- Intelligence, Simulation, and Modeling tion Proceedings462481-2484. monly available in the United States. does provide a broad survey of cur- Soo, V.-W.; Kulikowski, C. A.; Garfinkel, The authors begin by providing a rent research, but it is written from D.; and Garfinkel, L. 1988. Theory Forma- short but competent introduction to an AI perspective and will find a tion in Postulating Enzyme Kinetic Mech- the field of expert systems. Of partic- greater readership among AI researchers anisms: Reasoning with Constraints. ular value are the ideas regarding the Computers and Biomedical Research than simulationists. Because the field relevance of expert systems to busi- 21:381403 of integrated AI-simulation approaches ness and the idea that knowledge is know-how. Throughout the book, the is rapidly developing, future survey Mark E. Lacy is manager of computation- texts will be needed, and hopefully, al biology at Norwich Eaton Pharmaceuti- authors frequently tie their concepts they will be assembled for workers cals, Inc., Norwich, NY 13815. H to their relevance in a business set- both inside and outside AI. The work ting. The emphasis on human know- of long-time simulationist David how and its application in expert Garfinkel at the University of Penn- Expert Systems in Business: systems, as opposed to the less specif- sylvania and his colleagues on the A Practical Approach ic concept of human knowledge, sets integration of AI and simulation in the this presentation apart from others. study of enzyme kinetics (Garfinkel John Musgrove This book is clearly concerned with et al. 1987; Soo et al. 1988) is an business applications for expert systems example of work that should receive The cover to Expert Systemsin Business: as opposed to research applications. A more attention in future texts. A Practical Approach by Michael L. strong emphasis on application selec- Barrett and Annabel C. Beerel (Ellis The AI perspective is also shown in tion is made to assure that developers the references provided at the end of Horwood Limited, Chichester, Eng- focus their efforts on the benefits to land, 1988, 259 pages, $36.95, ISBN each chapter; they are up to date and an organization’s core business unit. O-7458-0269-9) contains an abstract useful in pointing to additional Four good checklists for application design in colors of violet, brilliant sources. However, the reference lists evaluation are addressed. Although do not include enough references to green, and dark magenta. Thus, the these checklists are good, they are book is difficult at first to take serious- reports that have appeared outside similar to other sources’ criteria for ly as a technical book. After seeing the AI and simulation literature, such selection and do not provide any as many of Garfinkel’s. other Ellis Horwood books, however, new concepts or considerations. The The material in this book is well it appears that the use of technicolor authors make a good presentation for organized but uneven in its accessi- covers is the publisher’s approach to the use of outside services for devel- product differentiation on the infor- bility to the reader. Some chapters oping initial applications to limit the mation technology bookshelf. are a joy to read, but others are need- costs and frustrations of starting a new Although this book presents many lessly technical and contain many endeavor within the organization. pages of unnecessary detail. This of the ideas and issues previously They also recommend developing an covered by others, particularly Paul result is not unusual when many dif- in-house knowledge Harmon and David King in Expert ferent authors contribute to a book, team for the long term but give it but it is an important factor that Systems: Artificial Intelligence in Busi- little attention. ness (Wiley, 1985), the authors present must be addressed to meet the needs I disagree with the authors’ con- their own experience in Great Britain. of the reader. The most appropriate tention that an expert system group In a short preface, Barrett and Beerel readership for a book of this type should not be made part of the data list 10 principal aims, among which includes AI researchers, simulation- processing department to avoid the are to identify the business benefits ists, and graduate students in AI or risk of having expert systems used as simulation. However, because of the that can be obtained (from applying just another software technique. I expert systems), provide a step-by- approach chosen by the editors and believe that expert systems are a com- the level of detail given by many of step strategy for identifying potential puter technique for solving problems expert system applications, and show the authors, this book would not just as other programming methods how an organization can get started make a good resource for many read- are. Also, truly useful expert systems in expert systems and achieve early ers other than AI researchers. are not wholly self-contained but Despite these shortcomings, this returns. Aims that the authors believe operate in conjunction with databas- are unique to their presentation book succeeded in making me aware es and other computer information include providing insight into how of a great deal of research. It has also systems. Earl Sacerdoti, formerly of experts participate in an expert made me anxious to see the first Teknowledge, recently observed that commercially available integrated system project and how they can the expert system industry has moved accomplish their tasks and giving a systems, so I can begin using one away from the Ptolemaic view that detailed account of knowledge engi- myself. Artificial Intelligence, Simula- the universe revolves around expert neering in practice, with a focus on tion, and Modeling is a welcome effort systems. The industry has moved to a what the knowledge engineer actual- that shows us some of the exciting Copernican view that expert systems computing technologies we have to ly does and how s/he must behave. are one solution among many that All the aims were addressed in the look forward to. revolve around business problems. book, although the chapters on soft- The technology section of this book ware and hardware were not tied to References provides a good explanation of the the aims and were not presented in inferencing process. Only Ken Peder- Garfinkel, D ; Kulikowski, C. A ; SOO,V.- sufficient depth. This problem might

SPRING 1991 101 Book Reviews sen’s Expert Systems Programming approach to developing the problem expert system technology and the (Wiley, 1989) provides a better discus- and the knowledge and implement- building of effective systems are valu- sion of the processes involved. The ing the solution. This presentation is able to anyone expecting to consider authors give a good description of the useful if you choose not to use an using expert systems in a business necessary criteria for software tool outside consultant, as recommended. environment. selection, focusing on the importance The two chapters on being a knowl- John Musgrove is group leader for expert of the user interface, the developer edge engineer and being an expert systems in the Houston regional office of interface, and the interfaces to external are unparalleled and effective. The Bechtel Corporation He evaluates expert computer systems. They review the authors discuss the selection and system applications and directs expert published methods of building systems. training of knowledge engineers. system development projects He received They present concise, three-paragraph Also discussed are issues concerning a B.S. in mechanical engineering from discussions of five approaches to how the knowledge engineer should Southern Methodist University in 1968 knowledge acquisition. Barrett and interact with the expert. Tricks of the and a M S in engineering from the Uni- Beerel include a substantial discus- trade for handling typical knowledge versity of Houston in 1973. W sion of uncertainty, but the concept engineering situations are presented. is dismissed because of the variation The authors’ discussions about being Genetic Algorithms in in implementation for different soft- an expert are unique and unavailable ware systems. The authors’ emphasis in similar books. The beneficial char- Search, Optimization, and on know-how systems leads them to acteristics of an expert are discussed Machine Learning preferentially recommend rule-based as are the expert’s possible emotions Terry Rooker production systems. throughout the project. The discussion of software tools is The final chapter is short but effec- Genetic algorithms are one of those the weakest part of this book. The tive. A much longer discussion of ideas that have been germinating for chapter on computer software is project management can be found in some time. They can trace their roots short, the product descriptions are Crafting Knowledge-Based Systems by J. to the mid-1960s. minimal, and the authors make no Walters and N. Nielsen (Wiley 1988). Genetic algorithms provide an comparisons. This situation might The authors provide a good descrip- alternative to traditional search tech- reflect the status of software availabil- tion for a project team for project niques by adapting mechanisms ity in Britain; it is not applicable to control and documentation. The pos- found in genetics. The basic idea is the American market. In contrast, sible distribution of the team effort- simple: The parameters of a problem Barrett and Beerel provide good crite- the cost in time and person-days-is are translated into some encoding, ria for software tool evaluations. The presented for a small project. usually represented as a binary string. discussions of computer hardware are The chapter “Conclusions and Rec- A population of strings is then ran- also generic and limited. The authors ommendations” comprises many domly generated. Some measure of seem to contradict themselves by good summaries. Here, as throughout fitness is then applied to each of the alternately saying “in the future we the book, the use of bulleted lists strings. The goodness of the fit deter- expect to see all workstation vendors helps readers focus on important mines the string’s chances to contin- offering delivery... via personal com- concepts and items. Although the ue influencing the search. The more puters” (p. 130) and “a likely pattern authors used extensive cross-referenc- fit the string, the more likely it is to for the future is development on per- ing to other chapters within respec- be selected to create part of the next sonal computers and delivery via tive sections, greater referencing to generation. mainframes” (p. 131). The relative chapters in other sections would A series of operators is used to roles of the large workstations and have been beneficial. create this next generation. Three mainframes versus personal comput- The list of references is short but common operators demonstrate the ers is left unresolved. Potential devel- includes both old and new material. selection procedure. First is reproduc- opers would do well to supplement The appendixes include the typical tion. This operator selects the strings this section with Expert Systems: Tools Glossary, Applications, Sources of to be operated on by the other opera- and Applications by P. Harmon, R. Information, and Example Costings. tors. The selection should be related Maus, and W. Morrissey (Wiley, 1988). The glossary is more than sufficient to the measured fitness of the strings. By contrast, the section on build- for a book of this kind. The list of Crossover is then used to combine ing effective systems is the strongest well-known applications is shorter parts of different strings. In its simplest part of the book. The implementa- than that of other books, but the broad form, a random point is selected, and tion overview is particularly good, list of potential application areas is the parts of the two strings beyond having been drawn from the authors’ singularly good. The sources of infor- this point are simply exchanged. experience in system building. The mation are limited and not current Mutation is the rare occurrence of different development styles avail- for U.S. periodicals. Again, this limi- changing one of the bit values. able, based on the goal of the effort, tation might result from the authors’ As the number of fit strings increas- are well presented. The presentation location in Great Britain. Additional es in the population, the more simi- and discussion of spider diagrams to evidence of the British source of this lar the strings are likely to be. In this represent areas of knowledge differs book is in the example castings; they case, the reproduction and crossover from other published presentations. are given in pounds sterling. make ever-finer distinctions in the The authors further define and devel- In summary, Expert Systems in Bzai- overall fitness of the strings. Eventu- op this concept in the chapter on ness: A Practical Approach is a good ally, a string is found that is good building the system. This chapter pre- addition and companion to other enough for the problem at hand. As sents a systematic and reasoned books on the subject. The sections on with many such methods, if it is left

102 AI MAGAZINE running long enough, an optimal lelism is two orders of magnitude been around for 25 years, there is a solution can be found. greater than the size of the input. small group of individuals research- There are numerous open ques- That is, for n strings in the popula- ing the various questions. They have tions. There is research on the operators tion, n*3 building blocks are evaluat- tremendous promise given such lim- to use, coding schemes, and evalua- ed. Such a speedup would have ited development. There has been a tion functions (measures of fitness). obvious effects on the efficiency of recent surge of interest in genetic This system is only a simple one to the search. algorithms, and maybe this renewed outline to concept. The computer science field in gen- interest will produce solutions to The interesting point about genetic eral and AI in particular have heard more of the questions. algorithms is that they are essentially many of these “too good to be true Goldberg’s Genetic Algorithms has variations of blind searches. There is claims.” The important question in proven to be the de facto handbook no operator or evaluation based on this case is whether genetic algo- of the field. He has written a concise, the next likely step. All evaluation of rithms can live up to their promise. detailed description of genetic algo- the current state is based simply on They have some theoretical advan- rithms. He uses the right mix of how well this state satisfies some tages over traditional techniques. To examples and theory. He starts with measure of fitness. truly evaluate their potential, a series simple descriptions and works up to As the search through the genetic of comparative experiments are the open questions. He is also frank algorithm space proceeds, the opera- needed. Under controlled circum- about the limitations of the approach tors are building short segments of stances, genetic algorithms and other and equally enthusiastic about its strings that each contribute to the algorithms can then be compared. promise. If you are interested in solution. These segments are appro- Unfortunately, such a comparison is genetic algorithms in any capacity, priately called building blocks. As they probably not possible. As previously then Genetic Algorithms is the best are discovered, they increase the over- explained, genetic algorithms only place to start. all fitness of their including string. If use an objective function. Most other the crossover operator destroys this techniques use some other informa- Terry Rooker is a system engineer at the building block in a string, the overall tion to determine the direction of Naval Surface Warfare Center in Dahlgren, Virginia. He is working as a system ana- fitness of the offspring should decrease. the search. There is a problem with lyst on a command and control system. n As the incidences of this building comparing such different methods. It block increase, it is more likely to be is never conclusive whether differ- included in new offspring. As this ences in performance are a function process continues, the strings get of the underlying functions or the closer to some optimum configuration. different algorithms. This publication is Genetic algorithms have some Goldberg discusses the performance available in microform advantages over other optimization of genetic algorithms for the traveling from University or blind-search techniques. Most salesman problem, a standard bench- optimization methods evaluate indi- mark for optimization. He points out vidual points and then try to evalu- that the genetic algorithm does not ate the best direction for the next perform as well as other methods but move. As David Goldberg points out reminds us that the genetic algo- in Genetic Algorithms in Search, Opti- rithm is also solving the problem mization, and Machine Learning (Addi- without information such as the city son-Wesley, Reading, Mass., 1989, distances. Once again, there is only a 412 pages, ISBN O-201-15767-5), con- measure of fitness and not any func- sidering only a single location at a tion to determine the next-best direc- time leaves such algorithms suscepti- tion for search. Any such comparison ble to getting caught in local minima of genetic algorithms with other (or maxima). Genetic algorithms approaches is always open to such avoid part of this problem by using comments. only an objective function (the mea- This comparison highlights the 3 Please send information about these titles: sure of fitness). Because there is no main strength of genetic algorithms. notion of direction in the search, They do not need domain-specific genetic algorithms do not need information, just some measure of NZXIE

derivative information or any of the how well each generation of strings Company/Institution complex methods for calculating the fits some criterion. There is no need to develop mathematical models so next-best move. Address Genetic algorithms also have that there is some function to use to my implicit parallelism: As the different evaluate the next step. There is no - building blocks are generated, the need for the computational overhead state Zip algorithm is essentially searching a of artificial neural networks that Phone [ I Call toll-free KKI-5'21~3044.h Michigan, large space of possible building implicitly perform such calculations Alaska and Hawaii call collect 313-761-4700 Or mall inquiry to: University Microfilms International. blocks. As the best building blocks inside the network. In such cases, 300 North Zeeb Road. Ann Arbor. MI 48106 are found, they are incorporated into even if not superior, the genetic more successful strings. In this sense, algorithms might prove easier to the search is proceeding in parallel. implement. Goldberg mentions that this paral- Although genetic algorithms have

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