This article will attempt to give a very brief overview of some of the technologies that make up the field of Artificial Intelligence.
Artificial Life (A-Life) is the study of artificial or computer based systems which exhibit life like behaviour. Computer simulations of individual agents or populations of agents can be used to investigate many of the properties of living systems. In some cases, mechanically constructed agents are provided with basic functionality and allowed to interact with real environments. This area of study is currently attracting considerable research interest and has generated two international conferences and spawned an international journal (Artificial Life: MIT Press).
The International Society of Artificial Life (ISAL) website looks to promote the use and development of ailife. The society is a paid membership one, but the links section of the website is open to all. This gives a comprehesive guide to some of the work going on in the area, from a list of research groups involved in ailife to where you can download software to undertake ailfe experiments.
Artificial Neural Networks
This technique has been applied to many of the difficult problems in AI with some success. For instance, Artificial Neural Networks have been used successfully in visual pattern recognition, even human faces and complex industrial components can be differentiated. Artificial Neural Networks have been used in speech recognition system to decipher audible language.The technique used is that of a highly parallel network of simple processing elements. Each element has some similarity with animal nerve or brain cells called neurons.
A very good plain english introduction can be found on the wikipedia website. The tutorial includes a code project and comes complete with commented source. Images are used to illustrate concepts. A PDF version is available on demand.
Case Based Reasoning
Case Based Reasoning (CBR) is a technique which is aimed at providing Expert Level advice to user queries in a similar way to the service offered by Knowledge Based Systems. However, with CBR it is not necessary to elicit Expert Knowledge but it is essential that a comprehensive data base of past problem solving examples is available. A CBR system will accept a new user query and then try to match this query with the entries from the data base. The best matches are selected and then further processed to find an answer to the current query.
For a comprehensive list of projects, applications and news of the Cased Based Reasoning area, visit the wikipedia website, founded by friend of AKRI Dr Ian Watson.
Data Mining is a term used to describe the process whereby software tools examine a company data base in order to locate information which may have complex parameter connectivity. Such information would normally be inaccessible to the human expert due to the enormous quantity of data and combinatorial tests which would have to be performed. A simple example may be a data base of company products and parameters which describe their applicability to various sectors of the market. A Data Mining system may discover that gaps exist within the companies product range and may also be able to describe the parameters which a new product should have in order to fill the market niche.
For a more in-depth introduction to Data mining Look at the wikipedia website. It has a lot of useful information including a downloadable booklet on many aspects of Data Mining. They also provide a useful glossary of Data Mining terms
It is often said that computers are too logical and that they can only deal in true or false, yes or no etc. However, Fuzzy Logic allows a computer to deal in everyday human language and actually process terms such as probably, unlikely, quite near etc. Such terms can take their place in computations, allowing the computer to arrive at verifiable results from fuzzy inputs. The logic used is mathematically verifiable, so results from the process can be trusted.
A good place to start with Fuzzy logic is the American Association for Artificial Intelligence's website. It provides many links to faq's, tutorials and online readings, as well as many Fuzzy Logic related websites.
There is also a more detailed explanation of Fuzzy Logic and its relationship to more traditional forms of Logic on this site in the article From Logic to Fuzzy Logic.
These are systems which attempt to mimic natures adaptive way of solving problems by the survival of the fittest. In computer systems, a population of random solutions to a problem is generated and then each solution in turn is evaluated to see how well it performs on a typical problem. When all of the population has been tested, various operators are used to select a new population. These operators include mating, that is, joining two of the best methods by crossing their genes to produce a new individual. They also include mutation where a gene of an individual is changed at random. The new population is tested again on typical problems and the process continues until a good solution evolves.
Intelligent User Interfaces
Systems which rely on an AI approach are intended to interact with the learner to provide a learning environment which suites the individual. This area relies on many other areas of AI but has become an area of active research following the greater availability of appropriate technology. Modern systems employ Hypertext, images, Sounds and Animations to produce a complete learning system. Hypertext frees the learner from the constraints of learning from lists of material, a common problem with books, older computer systems and some Teachers and Lecturers.
Knowledge Based Systems
Knowledge Based System allows a user to interact with a computer program in much the same way that the user would interact with a domain expert. The Expert System or Knowledge Based System (KBS) contains a well defined area of knowledge associated with a specific area of human expertise. The KBS also contains software that allows it to enter a dialogue with the user to clarify a particular problem and then offer appropriate advice.
It is possible to obtain KBSs that have no knowledge so that knowledge from a new domain can be entered. These empty expert systems (Shells) contain the software necessary to bring the knowledge to life and also contain some sort of user interface.
KBSs can offer a very useful way to make scarce knowledge available to many people and also to put knowledge at the point of delivery. They can also help a company to protect some of its knowledge asset.
Increasingly, companies are realising that the knowledge asset possessed by staff and their knowledge of customers, products and processes, are assets equal in value to other assets such as machinery, equipment or estate. The inevitable conclusion is that a companys knowledge asset must be managed at least as well as any other asset.
Knowledge Management is therefore: the process by which a company values its knowledge resource and seeks to manage it effectively within the main stream of company activities.
Knowledge Management is primarily focused on knowledge possessed by people.
Some commentators on knowledge management also include systems that store and process information, such as databases, knowledge bases and distributed information systems. Knowledge can be lost in many ways,
- Through retirement, redundancy or even through promotion when knowledge may become unavailable.
- Through overloading where experts cannot function properly because they are too busy.
- Through a culture of blame where staff are reluctant to apply their knowledge in new ways.
The competitive nature of modern business means that knowledge is a vital resource which must be managed and applied properly. Its development should not be left to chance in case development turns into demise.