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Review of Artificial Intelligence, Simulation, and Modeling Book Reviews AI Magazine Volume 12 Number 1 (1991) (© AAAI) BookReviews Artificial Intelligence, Numeric simulation 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 system scientist doing modeling ent as well: AI tools do not lend cally, the systems 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 engineering 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.
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