Conversion in Progress

Chapter 6
Cross Fertilization in AI & DS

This chapter examines underlying common elements in AI and DS and also looks at combinations of theory and technique. It will be shown that these common elements and combinations can be synthesized to create an enhanced approach toward problem solving. The following sections address the issue of technique commonality. They do this by; 1) combining elements in purpose, 2) addressing common underlying theory, and 3) establishing an awareness of specific techniques which accomplish "like" objectives.The last part of this chapter examines a number of examples where AI has been added to DS based systems and vice versa. The systems examined include real world implementations, prototypes and commercial software packages. A Combined Purpose The beginnings of chapters 4 and 5 outlined the principle characteristics of AI and DS systems. By combining these characteristics we find our new AI/DS goals to be; the enhancement of algorithmic methods with heuristic extensions, the extension of analysis by satisficing goals where optimization is not possible or practical, the addition of qualitative/symbolic techniques to a model which is largely quantitative. In the instances above the reverse should also prove true, i.e. AI methods can, in many cases, be improved via DS technology. For example, in early AI languages such as LISP and PROLOG the handling of math variables and the accessing of databases was very difficult. After much complaining by analysts & programmers, vendors and language creators made numeric and large data handling an integral part of their products. Another example is the proposed use of AHP to help determine the value of certainty factors in Expert Systems. [ES2] An interesting sidebar to any effort to combine these fundamental purposes is the preexisting commonality in the underlying nature of the two areas. There are a wide variety of techniques available in the AI and DS worlds. Andriole[GN1] has catalogued over 1000. And Hopple[DS3] has created a taxonomy for classification of them. It is shown below. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-1. Hopples Taxonomy of Techniques Why look at commonality and/or extensionality in AI/DS techniques? There are 2 major potential problems with the use of techniques. The first is that we need to be sure we do not get trapped into what Hopple terms "Methodoliatry". This is where we attempt to use one technique to solve all problems. As the adage goes "To a three year old with a hammer, all the world is a nail." The second is that we do not spend too much time reinventing the wheel the Not Invented Here (NIH) syndrome. Overcoming these problems is difficult because it requires a large body of knowledge on both what techniques are available and on how to apply them. Researchers including Andriole[GN1], Nunamaker[GN8], and Westling[GN13] have researched and built systems to help mitigate this situation. Despite their work, differentiating among techniques is difficult. Potential solutions to this problem are discussed in the final chapter. We all know that there are many ways to travel from here to California, e.g. by plane, train, bus, car, feet. Which method is better? What criteria exist for establishing the "better" method? Do we use processing time? processing cost? simplicity of the technique? validity & reliability of the solution? least cognitive dissonance? all of these? If so, how do we discover and/or test our evaluation, especially when minor differences exist, and substitutions may prove equally effective from a body of thousands of techniques?There is no easy answer except perhaps a better understanding of the fundamental thrust in the individual areas. The following diagram outlines some of the criteria which may be helpful in determining commonality and/or differences within technique classes.

                Neural Net           Decision System        Expert System         

Learning        some                 none                   none                  

Problem         limited              broad scope            moderate scope        

Repeatable      part of purpose      usually unique         part of purpose       

Complexity of   black box; non       simple & complex       simple; linear;       
Problem         linear               linear & non linear    heuristic             
Solution                             algorithmic                                  

Ability to      built in             poor; model needs      none                  
handle                               respecification                              

Time and        short; moderate      depends on technique   short to long;        
difficulty to                                               moderate              

Figure 6-2. Technique Class Attribute ComparisonUnderlying Commonality in Axioms & Theory The overlap in AI and DS methods and theory exist fromboth from a practical standpoint and a theoretical one. From a practical standpoint there is an overlap in three principal areas. These include the extensive use of the computer for data storage and access, the computational burden of numeric or symbolic manipulation, and the heavy use of modeling. In a more theoretical vein, White[GN14] pointed out the overlap in the conceptual bases of graphs, networks and search. To this should be added the topics of subjectivity and uncertainty.ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿³ Graph & Network Theory ³³ Search Algorithms ³³ Uncertainty Handling ³³ Subjectivity Measures ³³ ³³Figure 6-3. Theoretical Underpinnings in AI and DS ³ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙ Graph theory is a heavily researched area in the decision sciences. This is also true of AI because the underlying knowledge representation mechanisms semantic nets, frames, and neural networks have graphs as their basis. Classic operations research problems such as the traveling salesman problem and source to destination routing utilize various search algorithms to minimize distances. Likewise in the AI discipline "Search problems are ubiquitous, popping up everywhere Artificial Intelligence researchers and students go"[Winston [AI11] p. 87]. The handling of uncertainty is a large domain within generic OR to include classic probability, decision trees, and bayesian methods. Virtually all statistical methods utilize probability distributions as a partial measure of uncertainty. In artificial intelligence we find certainty factors, Dempster Shafer techniques, and fuzzy logic, all for dealing with inexact reasoning. See Ng and Abramson[GN7] for a discussion of 6 techniques for the application of uncertainty in expert systems, 3 of which are in common use in decision systems. Finally subjectivity measures exist strongly within the decision sciences in the form of risk preference, and within AI as the very basis of AI design -resident in rules, frames or nets. Both practitioners and theoreticians need to be aware of these common elements to eliminate unnecessary incursions into research areas which have already been explored. Some examples of that duplicity follow. Commonality/Extensionality in Techniques AI and DSs cannot escape the old cliche "There is nothing new under the sun." For a number of these techniques, while perhaps having radically different origins, accomplish virtually identical tasks with equal alacrity. Some of these examples follow in the table below. Lipppman, in the NeuralWorks manual, provides a list of statistical methods compared to their NN counterparts. The Anderson et al article reviews in some depth the use of eigenvector/ eigenvalue processing for feature analysis in neural network systems. Lawrence provides a critique of the Brainmaker software package, but also discusses the use of this tool as a "fully automated nonlinear multidimensional regression analysis tool". He also addresses the use of NNs against Non Polynominal (NP) problems. Odom looks at a comparative study of back propagation "generalization" (using Neuroshell) against multivariate discriminant analysis (MDA) (using the SAS DISCRIM procedure). White explains that back propagation is identical to a statistical procedure called "stochastic approximation method". He differentiates the techniques by such characteristics as binary v. continuous valued input, supervised v. unsupervised learning, and pattern type. Rendell tests 6 different learning methods including curve fitting, response surface fitting, and genetic algorithms. ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿³AI Based DS Based Reference ³³ ³³Neural Nets Statistical Methods Lippman [S4] ³³Neural Nets AHP Anderson[S1] ³³Neural Nets NonLinear Multiple Lawrence [S6] ³³ Regression ³³Neural Nets Multivariate Odom [S8] ³³ Discriminant Analysis ³³Neural Nets Stochastic Approx. White [S10] ³³Genetic Curve Fitting Rendell [] ³³ Algorithms ³³Learning Explor. Data Analy. Fisher&Langley ³³ Algorithms ³³Neural Nets Correlation Xenakis [S11] ³³Neural Nets Bayes Classifier Guyver[] ³³Constraint Integer Programming Dhar &Rang. ³³ Satisfaction [RA2] ³³Rules Algebraic Fordyce [GN5] ³³ Representation ³ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙFigure 6-4. AI & DS Techniques w/ similar Bases or Purpose We have now examined the issue of commonality. It is now time to turn to looking at specific supporting examples in both the research and commercial sectors. Practical AI applied to DS What follows is an examination of 38 systems or proposals for systems. Ten of these come from "in place", real world systems. Twenty could best be labeled research prototypes or proposed, detailed directions for applying AI to DS. In addition eight commercial software packages, which in some form provide an enhanced AI capability to the fundamental DS function, are examined. These systems exist in all DS areas including MCDM, statistics, forecasting, linear programming, planning, scheduling, queuing, and project management. It should be understood, however that in some cases the thrust of the real world or prototype system really emanates from the AI world, and the minor emphasis is on DS. The order of presentation follows the 1) real world, 2) prototype or proposed, and 3) software format already established. The table below provides a complete list. REAL WORLD AI Based DS Based System Name Reference 1) Rules MCDM n/a Levine et al 2) Rules MAUT n/a Madey et al 3) Expert System Statistics PREVISTA Walker&Miller 4) Rules Statistics REX Gale 5) Frames Statistics ZEERA Marcoulides 6) Expert System Forecasting IFFX Walker & Miller 7) Expert System Forecasting EMEX Walker & Miller p. 158 8) Expert System Forecasting SMARTFORECAST Walker & Miller p. 192 9) Rules Resource Alloc. n/a Levine & Pomerol10) Expert System Project Mgmt XPM Walker & Miller p. 189PROTOTYPES/PROPOSALS AI Based DS Based System Name Reference 1) Rules Decision Analy. n/a Holtzmann 2) Neural Nets Assessment MCDM n/a Wang 3) Neural Nets MAUT n/a Madey 4) Learning MAUT n/a Madni et al 5) Fuzzy Logic Cost/Effectiv. n/a Dockery 6) Predicate Logic Decision Theory n/a Fox et al 7) Induction Decision Trees n/a Quinlan 8) Rules & NL Statistics IS Remus 9) Inference Statistics/Math AR Lacy10) Expert System Statistics ASA Walker & Miller p. 21111) Expert System Statistics CARDS Walker & Miller p. 21112) Expert System Statistics Experiplan Walker & Miller p. 21213) Expert System Forecasting n/a Kumar & Hsu 14) Expert System Forecasting n/a Kuo 15) Neural Nets Linear Progr. n/a Mort16) Rules Linear Progr. n/a Murphy and Stohr17) Frames Linear Progr. n/a Binbasioglu/Jarke18) Semantic Nets Linear Progr. n/a Evans and Camm 19) Natural Lang. Queuing NLPQ Feignebaum20) Expert System Queuing SQS Hossein et al.SOFTWARE AI Based DS Based System Name 1) Cognitive MCDM SmartEdge 2) Rules Matrix MCDM Lightyear 3) Induction Matrix MCDM Expert 87 4) Cognitive AHP MCDM Expert Choice 5) Expert Systems Statistics Statistical Navigator 6) Expert Systems Statistics Knowledge Seeker 7) Expert Systems Project Mgmt Proj. Mgmt. Advantage 8) Expert System Influence Diag. AIDA Figure 6-5. Existing AI/DS Systems Some caution need be expressed when looking at these real world and proposed systems. For example, the systems either proposed or built by Remus & Kotteman, Kumar & Hsu, as well as the software products Statistical Navigator and Knowledge Seeker all fall into the category of advisory systems. That is, they simply prescribe a statistical or forecasting technique based upon some descriptions of what the user needs, and a description of the data. Obviously a similar system could be built for guidance through the family of linear programming techniques and other disciplines in the OR/MS world. There is therefore little technological symbiosis here, just a simple treelike advisor in a black box. However there are systems which have been created which do exhibit a powerful melding of the AI and DS disciplines. Some of these follow.Some Real World Systems Four "real world" projects in which technological symbiosis is particularly evident include the Madey et al contract bidding system, the Levine & Pomerol French Railway system, the Levine, Pomerol, and Saneh postal sorting machine selection, and the Gale et al REX system. Madey et al developed a combination expert system/ multiattribute utility model to help an aerospace firm bid on selected projects. Resident in the expert system component are certain guidelines revolving around financial areas such as npv and payback, and around technical areas such as estimating work involved. The MAUT component acts as an evaluation function. Ultimately the user is provided with a continuous versus discrete score for ranking of the projects. Levine and Pomerol were concerned with railcar distribution throughout France's 33 nationwide regions. The use of strictly linear programming was considered, but left 2 gaps the first in computational time and the second in top level strategy. Processing of the routing of 100 different types of railcars, using 1056 variables in 33 regions, etc. etc. required an all night LP run to come up with a static solution. When sticky problems would occur after the run, the operators would use some top level reasoning to tweak the new routings. The new system combines a traditional allocation algorithm with operator level reasoning placed in schema trees. These trees define the priority of requests and arising deficiencies. Diagrams of the general architecture and a schema tree are shown below. Figure 6-6A. Railcar System Architecture Figure 6-6B. Region Schema Tree Levine, Pomerol and Saneh used an interesting transform of rules to a decision matrix to help the postal service select a sorting machine. The essence of this system is a set of rules which analyze data to either aggregate some subcriteria qualities or to evaluate nonquantitative criteria such as political risk. That is the system begins with raw data and produces an matrix with established values. An example of some of the rules and the resultant decision matrix are depicted below. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º Postal 1 º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-7A. Rules for Sorting Machine Selection Figure 6-7B. Decision Matrix Derived from Rules The REX system was built by Gale and Pregibon at Bell Labs. REX is used to guide a user through building a regression analysis. It extends traditional advisory systems in that it conducts an ongoing dialogue with the user while performing data analysis. Some features that it offers include depth first search of problem possibilities, test interpretation including a lexicon, and graphics with interpretation. Test offerings including granularity, extremepoint analysis, spacing analysis, missing data analysis, and skewness tests. As Gale states "Existing statistical software, although quite powerful, requires considerable statistical knowledge to be used effectively. The capability to serve more people would be the basis for a viable product because it would increase productivity and reduce training requirements." The diagram below depicts a screen from a session with REX. Figure 6-8. Screen of Session with REX Statistics is an interesting area for the application of AI techniques. Inferential statistics could be viewed as a marriage of inductive and deductive reasoning. The number crunching to produce a statistic from data is an inductive process. And the purposeful limitation of the scope of application through a hypothesis test is recognized as deduction. The origin of some of the mainstream research in inductive reasoning follows from basic statistical analysis such as hypothesis testing of a single dependent variable. See Barr & Feigenbaum[AI4] for a description of BACON, CLS, and ID3 three inductive reasoning systems. Some Prototypes and Proposals A number of interesting prototypes and system proposals exist. Five of these, which deal with a wide array of possibilities, will be highlighted here. Three of these deal with applying learning, in two cases within the realm of MCDM, and the third as applied to limited resource allocation. Another example looks at using natural language processing for creating queuing models. And the last example utilizes a combined expert system/Bayesian model to support forecasting. Learning in neural networks (NNs) has become a hot topic in the scientific world. The effects of applying it to decision making shows promise. Wang[MC13] has proposed a model for using NNs as an assessment technique in MCDM. As he states "The motivation of this approach is to capture the essence of the decision makers rational preferential behavior with artificial neural networks via supervised learning." While other accepted methods now elicit preference behavior, two weaknesses in these other approaches include conditional independence of attributes, and inflexibility in the defined, a priori state. White [GN14] examines this same problem, pointing to the use of rules as a partial solution. Wang's method creates a mapping mechanism resembling the decision maker's preference behavior. His mechanism should be contrasted to decomposition methods now in common use. It shows superiority in assumptions of independence, and in immunity to noisy data. Weaknesses however arise when examining the likely limited data set which must be used for training. Figure 69 below portrays this system. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-9. Neural Network Preference Assessment Model Madni et al proposed an interesting system which combines the advantages of an adaptive learning model with the attributes of a MCDM model. These researchers wished to establish a model which would filter relevant messages from a large incoming stream. A user would begin by evaluating a message by applying weights to a set of criteria for judging the value of that message. These criteria included content area, age, specificity, familiarity, precedence, and locale.This process would be repeated for a series of messages. At the same time each message would be evaluated on a rank order basis, establishing its place within the message set. Based upon previous weightings, the system would also estimate the message ranking. Differences between the user's rank and the system rank would be used to adjust the attribute weight factors. Once the system error margin had been reduced to an acceptable level, new incoming messages could then be channeled by the system. Figure 610 below portrays this system. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-10. Message Attribute Training Schematic Mort has suggested the application of artificial neural networks to limited resource allocation. Unlike traditional DS methods, such as integer programming, Mort's method uses a time dimension. This time dimension is used to apply learning. His system contains the elements of system effectiveness, constraints, and how to best respond. System effectiveness corresponds roughly to an objective function evaluation in traditional mathematical programming. Constraints act in a manner identical to their mathematical programming cousins. Since this is a time based system, resources may be released (as responses) in different time periods. Mort has postulated a unique approach toward understanding system effectiveness which he calls "differential ratio learning". It is based on a common neural network learning algorithm developed by Hebb[NN1]. While Mort's approach is of limited value in a static environment, it would have application in environments with changing available resource levels and changing goals. As an example, he uses it in a battle scenario where force levels and threats may be very dynamic. Figures 611A and 6-11B below provide a depiction of his approach. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-11A. Layered Limited Resources Network ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-11B. Neural Network Structure The Natural Language Programming for Queuing (NLPQ)[Q2] simulation was created by George Heidorn, who is now with IBM. Heidorn's system takes English statements and builds a GPSS program to simulate a queuing situation. Underlying the system are production rules which guide the question and answer series, and build the program. The process includes about 300 English decoding rules and 500 English and GPSS encoding rules. Through the decoding rules, the Q&A session guides the building of a semantic network, which becomes the internal description of the problem (IDP). Once the IDP is built, the encoding rules create the GPSS simulation. Figure 612 below is an example of a conversation with NLPQ. ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º º ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍͼ Figure 6-12. NLPQ ConversationSome Off the Shelf SoftwareThis section will look at 5 off the shelf software packages which have some combined elements of DS and AI. The first three are MCDM packages with some overt or inherent AI functionality. The last two are packaged expert systems which deal with DS topics. The packages are respectively, Lightyear, Expert Choice, Expert 87, Project Management Advantage, and Statistical Navigator. LightyearLightyear (LY) is a matrix based multiple criteria decision making package. Unlike other MCDM packages on the market, LY has a rule capability. The rule capability may be used for alternative elimination, or adding/subtracting weight to/from an alternative's score. Despite the general classification of rules as AI, it could be persuasively argued that this capability provides little in the way of "intelligence" in this software. There is no inference engine to interpret cascading rules. Each rule is simply evaluated one by one against each alternative. Figure 613A below provides an example set of rules. Figure 613B shows the rule creation process. Figure 6-13A. Rules in Lightyear Figure 6-13B. Creation of Rules in LightyearExpert ChoiceExpert Choice (EC) is an AHP based MCDM package. The AI features in EC are less overt than in the other packages. Three features point toward artificial intelligence. These are a short term memory feature, the inherent concept of relative pairwise comparison, and a consistency measure. Expert Choice limits the number of attributes which may be considered on a level to seven in deference to Miller's research[CL5]. The concept of pairwise relative comparison has been shown by Saaty[MC11] to be cognitively superior to other MCDM value applying methods and is recognized by Hofstadter as a primary criteria for intelligence. And finally a measure is builtin which evaluates a user's consistency when making pairwise comparisons. It would indicate to the user if inconsistency is present, and allow them, if they felt it appropriate, to reevaluate the options. Expert 87 Expert 87 (E87) is also a matrix based MCDM package. It utilizes an inductive technique termed Social Judgement Theory (SJT) for eliciting preference assessments. The creators of E87 bill this technique as an "intuitive" method for applying weights to attributes. The user is presented with combinations of criteria and asked to grade the combination as a whole. Figure 6-14 provides a view of the SJT elicitation process. Figure 6-14. Expert 87 SJT Elicitation ProcessProject Management Advantage PMA is a an expert system which provides generic advice. It was written in an expert system shell called "1stClass", which utilizes an example based approach. Packaged with the expert system are some spreadsheets which create typical PM tools such as a Work Breakdown Schedule and an Earned Value Analysis. PMA divides its advice into 6 phases including Definition & Justification Planning and Budgeting Design Development/Construction Launch Preparation Delivery & Conclusion Each phase has approximately 10 small knowledge bases (KB) upon which question and answer sequences are drawn. These KB range in mission from creating novel concepts to printing out contracts between the project sponsors and the project leader. Some do simple math calculations such as expected time estimates and probability of completion computations. Statistical NavigatorStatistical Navigator is another expert system which provides generic advice. It was written in an expert system shell called "EXSYS", which is a rule based system. SN also accesses some external programs to do some calculations. The program asks for assumptions and for your objectives. During this process confused users may ask for an explanation. The SN output is a report on which statistical tests seem to best fit the described situation.A Summary of Commercial PackagesAn analysis of these packages using Hofstadter's characteristics of intelligence follows. As can be seen no full implementation (or approaching full) of any of these exist. Does the system (not the user) have: LY EC E7 PMA SNFlexible response ability N N P P PFortuitous circumstance recognition N N N N NAmbiguous/contradictory message recogn. N P P N NRelative importance recognition N P N N NSimilarities recognition P P P P PDistinction recognition P P P P POld & new concept synthesis N N N N NNovel idea generation N N N P PF Full Implementation P Partial Implementation N NoneGenerally, progress toward commercially available PC based packages combining AI and DS techniques has been slow. A number of reasons account for this problem, many of which are highlighted in the following chapter.Chapter Conclusions As has been demonstrated, research efforts to combine the powers of AI and DS are growing. But little of what has been created, and even less of what has emigrated from the laboratory capitalizes upon the strengths in each area. While the DS side of the house has matured, bringing strong AI into the mainstream to help DS remains a weak link. AI researchers will need to make AI techniques more accessible. This and other similar issues are addressed in the next chapter.