Editing Expert system (section)

== Disadvantages ==
The most common disadvantage cited for expert systems in the academic literature is the [[knowledge acquisition]] problem. Obtaining the time of domain experts for any software application is always difficult, but for expert systems it was especially difficult because the experts were by definition highly valued and in constant demand by the organization. As a result of this problem, a great deal of research in the later years of expert systems was focused on tools for knowledge acquisition, to help automate the process of designing, debugging, and maintaining rules defined by experts. However, when looking at the life-cycle of expert systems in actual use, other problems – essentially the same problems as those of any other large system – seem at least as critical as knowledge acquisition: integration, access to large databases, and performance.<ref>{{cite book |last1=Kendal |first1=S.L. |last2=Creen |first2=M. |year=2007 |title=An introduction to knowledge engineering |publisher=Springer |location=London |isbn=978-1-84628-475-5 |oclc=70987401}}</ref><ref name="Feigenbaum1983">{{cite book |last1=Feigenbaum |first1=Edward A. |author1-link=Edward Feigenbaum |last2=McCorduck |first2=Pamela |author2-link=Pamela McCorduck |year=1983 |title=The fifth generation |edition=1st |publisher=[[Addison-Wesley]] |location=Reading, Massachusetts |isbn=978-0-201-11519-2 |oclc=9324691}}</ref>

Performance could be especially problematic because early expert systems were built using tools (such as earlier Lisp versions) that interpreted code expressions without first compiling them. This provided a powerful development environment, but with the drawback that it was virtually impossible to match the efficiency of the fastest compiled languages (such as [[C (programming language)|C]]). System and database integration were difficult for early expert systems because the tools were mostly in languages and platforms that were neither familiar to nor welcome in most corporate IT environments – programming languages such as Lisp and Prolog, and hardware platforms such as [[Lisp machine]]s and personal computers. As a result, much effort in the later stages of expert system tool development was focused on integrating with legacy environments such as [[COBOL]] and large database systems, and on porting to more standard platforms. These issues were resolved mainly by the client–server paradigm shift, as PCs were gradually accepted in the IT environment as a legitimate platform for serious business system development and as affordable [[minicomputer]] servers provided the processing power needed for AI applications.<ref name="Wong 1995 141–152"/>

Another major challenge of expert systems emerges when the size of the knowledge base increases. This causes the processing complexity to increase. For instance, when an expert system with 100 million rules was envisioned as the ultimate expert system, it became obvious that such system would be too complex and it would face too many computational problems.<ref>{{cite book |last1=Lenat |first1=Douglas |author1-link=Douglas Lenat |chapter=On the thresholds of knowledge |title=Foundations Of Artificial Intelligence |editor1-last=Kirsh |editor1-first=David |publisher=MIT Press |pages=185–250 |year=1992}}</ref> An inference engine would have to be able to process huge numbers of rules to reach a decision.

How to verify that decision rules are consistent with each other is also a challenge when there are too many rules. Usually such problem leads to a [[satisfiability]] (SAT) formulation.<ref>{{cite conference |vauthors=Bezem M |work=9th International Conference on Automated Deduction |title=Consistency of rule-based expert systems |volume=310 |date=1988 |pages=151–161 |doi=10.1007/BFb0012830 |isbn=3-540-19343-X |series=Lecture Notes in Computer Science |url=https://ir.cwi.nl/pub/6113|url-access=subscription }}</ref> This is a well-known NP-complete problem [[Boolean satisfiability problem]]. If we assume only [[binary data|binary variables]], say n of them, and then the corresponding search space is of size 2<math>^{n}</math>. Thus, the search space can grow exponentially.

There are also questions on how to prioritize the use of the rules to operate more efficiently, or how to resolve ambiguities (for instance, if there are too many else-if sub-structures within one rule) and so on.<ref>{{cite journal |vauthors=Mak B, Schmitt BH, and Lyytinen K |title=User participation in knowledge update of expert systems |journal=Information & Management |date=1997 |volume=32 |issue=2 |pages=55–63 |doi=10.1016/S0378-7206(96)00010-9 |doi-access=free}}</ref>

Other problems are related to the [[overfitting]] and [[overgeneralization]] effects when using known facts and trying to generalize to other cases not described explicitly in the knowledge base. Such problems exist with methods that employ machine learning approaches too.<ref>{{cite book |vauthors=Pham HN, Triantaphyllou E |title=Soft Computing for Knowledge Discovery and Data Mining |chapter=The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining |date=2008 |pages=391–431 |doi=10.1007/978-0-387-69935-6_16 |isbn=978-0-387-69934-9 |s2cid=12628921 |chapter-url=https://digitalcommons.lsu.edu/cgi/viewcontent.cgi?article=4334&context=gradschool_dissertations}}</ref><ref>{{cite journal |vauthors=Pham HN, Triantaphyllou E |title=Prediction of diabetes by employing a new data mining approach which balances fitting and generalization |journal=Computer and Inf. Science G |date=2008 |pages=11–26}}</ref>

Another problem related to the knowledge base is how to make updates of its knowledge quickly and effectively.<ref>{{cite journal |vauthors=Shan N, and Ziarko W |title=Data-based acquisition and incremental modification of classification rules |journal=Computational Intelligence |date=1995 |volume=11 |issue=2 |pages=357–370 |doi=10.1111/j.1467-8640.1995.tb00038.x |s2cid=38974914}}</ref><ref>{{cite journal |vauthors=Coats PK |title=Why expert systems fail |journal=Financial Management |volume=17 |issue=3 |date=1988 |pages=77–86 |jstor=3666074}}</ref><ref>{{cite journal |vauthors=Hendriks PH, and Vriens DJ |title=Knowledge-based systems and knowledge management: friends or foes? |journal=Information & Management |date=1999 |volume=35 |issue=2 |pages=113–125 |doi=10.1016/S0378-7206(98)00080-9 |url=https://repository.ubn.ru.nl/handle/2066/240554|hdl=2066/240554 |hdl-access=free }}</ref> Also how to add a new piece of knowledge (i.e., where to add it among many rules) is challenging. Modern approaches that rely on machine learning methods are easier in this regard.{{Citation needed|date=October 2019}}

Because of the above challenges, it became clear that new approaches to AI were required instead of rule-based technologies. These new approaches are based on the use of machine learning techniques, along with the use of feedback mechanisms.<ref name="CADsurvey"/>

The key challenges that expert systems in medicine (if one considers computer-aided diagnostic systems as modern expert systems), and perhaps in other application domains, include issues related to aspects such as: big data, existing regulations, healthcare practice, various algorithmic issues, and system assessment.<ref>{{cite journal |vauthors=Yanase J, Triantaphyllou E |title=The Seven Key Challenges for the Future of Computer-Aided Diagnosis in Medicine |journal=International Journal of Medical Informatics |volume=129 |pages=413–422 |date=2019 |doi=10.1016/j.ijmedinf.2019.06.017 |pmid=31445285 |s2cid=198287435}}</ref>

Finally, the following disadvantages of using expert systems can be summarized:<ref name=":0"/>
# Expert systems have superficial knowledge, and a simple task can potentially become computationally expensive. 
# Expert systems require knowledge engineers to input the data, data acquisition is very hard. 
# The expert system may choose the most inappropriate method for solving a particular problem. 
# Problems of ethics in the use of any form of AI are very relevant at present. 
# It is a closed world with specific knowledge, in which there is no deep perception of concepts and their interrelationships until an expert provides them.