Computer systems are becoming commonplace; indeed, they are almost ubiquitous. We find them central to the functioning of most business, governmental, military, environmental, and health-care organizations. They are also a part of many educational and training programs. But these computer systems, while increasingly affecting our lives, are rigid, complex and incapable of rapid change. To help us and our organizations cope with the unpredictable eventualities of an ever-more volatile world, these systems need capabilities that will enable them to adapt readily to change. They need to be intelligent. Our national competitiveness depends increasingly on capacities for accessing, processing, and analyzing information. The computer systems used for such purposes must also be intelligent. Health-care providers require easy access to information systems so they can track health-care delivery and identify the most recent and effective medical treatments for their patients' conditions. Crisis management teams must be able to explore alternative courses of action and support decision making. Educators need systems that adapt to a student's individual needs and abilities. Businesses require flexible manufacturing and software design aids to maintain their leadership position in information technology, and to regain it in manufacturing. (Grosz and Davis, 1994)

The history of artificial intelligence (AI) predates the development of the first computing machines. On a general level, intelligence has been the subject of philosophical study for 2000 years. At the computational level, mathematician Alan Turing constructed a framework for AI during the era of analog computers.

While precise definitions are still the subject of debate, AI may be usefully thought of as the branch of computer science that is concerned with the automation of intelligent behavior. The intent of AI is to develop systems that have the ability to perceive and to learn, to accomplish physical tasks, and to emulate human decision making. AI seeks to design and develop intelligent agents as well as to understand them. Currently, the main fields of research and development include the following:

1. Natural languages: These studies focus on problems related to natural language interface, machine translation, understanding spoken language, and so forth.
2. Expert systems: No generalizable solutions are researched, but expertise is used to deal with ill-defined problems and relationships.
3. Cognition and learning: Investigations are being made into modes of thinking, learning, and problem solving.
4. Computer vision: Efforts are being made to develop principles and algorithms for machine vision and the interpretation of visual data.
5. Automatic deduction: This area deals with the resolution of problems, theorem proving, and logic programming.

FOUNDATIONS

The term "AI" was applied about 1956, giving a formal name to work that had been developing over the previous five or six years. Individuals and organizations have an abiding interest in AI for several important reasons, including the following:

1. To preserve expertise that might be lost when an acknowledged expert is unavailable.
2. To create organizational knowledge bases so that others may learn from past problem-solving successes.
3. To help decision makers be consistent in their evaluation of complex problems.

During its early years AI was dominated by reliance on logic as a means of representing knowledge and on logical inference as the primary mechanism for intelligent reasoning. In the 1990s other paradigms arrived on the scene, some of which had a dramatic impact. Artificial neural networks (ANNs) were motivated by assumptions about how the brain functions— particularly the ideas of massively parallel connections, each of which performs simple computational tasks. Taken together, they represent knowledge as a property of patterns of relationships. Genetic algorithms apply principles of biological evolution to the problems of searching complex solution spaces. The programs do not use logical reasoning either, but evolve toward better and better solutions to complex problems.

Multiagent systems have recently come to the fore of AI research. This emergence has been driven by a recognition that intelligence may be reflected by the collective behaviors of large numbers of very simple interacting members of a community of agents. These agents can be computers, software modules, or virtually any object that can perceive aspects of its environment and proceed in a rational way toward accomplishing a goal.

A variety of disciplines have influenced the development of AI. These include philosophy (logic), mathematics (intractibility, computability, algorithms), psychology (cognition), engineering (computer hardware and software), and linguistics (knowledge representation and natural-language processing).

Long before the development of computers, the notion that thinking was a form of computation motivated the formalization of logic. These efforts continue today. Graph theory provided the architecture for searching a solution space for a problem. Operations research, with its focus on optimization algorithms, used graph theory and other methods to solve complex decision-making problems.

In 1950, Alan Turing proposed what has become known as the Turing Test for defining intelligent behavior. The idea was to specify requirements that a computer would have to exhibit in order to demonstrate intelligence. Briefly, the Turing Test proposes that the computer should be interrogated via telecommunications by a human. Intelligence is exhibited by the computer if the interrogator cannot tell whether there is a human or a computer at the other end. In order to pass the test, a computer would need to have capabilities for natural-language processing, knowledge representation, automated reasoning, and machine learning.

AN EVOLUTION OF APPLICATIONS

While computer systems have become commonplace, they are generally rigid, complex, and incapable of rapid change. According to A Report to ARPA on Twenty-First Century Intelligent Systems, for us and our organizations to cope with the unpredictable eventualities of an ever-more volatile world, these systems need capabilities that will enable them to adapt readily to change. The report argues that our national competitiveness depends increasingly on capacities for accessing, processing, and analyzing information (Grosz and Davis, 1994).

One of the early milestones in AI was Newell and Simon's General Problem Solver (GPS). The program was designed to imitate human problem-solving methods. This and other developments such as Logic Theorist and the Geometry Theorem Prover generated enthusiasm for the future of AI. Simon went so far as to assert that in the near-term future the problems that computers could solve would be coextensive with the range of problems to which the human mind has been applied.

Soon difficulties in achieving this objective began to manifest themselves. In scaling up from earlier successes, problems of intractability were encountered. A search for alternative approaches led to attempts to solve typically occurring cases in narrow areas of expertise. This prompted the development of expert systems. A seminal model was MYCIN, developed to diagnose blood infections. Having about 450 rules, MYCIN was able to perform as well as many experts. This and other expert-systems research led to the first commercial expert system, R1, implemented at Digital Equipment Corporation (DEC) to help configure orders for new computer systems. Sub-sequent to R1's implementation, it was estimated to save DEC about $40 million a year.

Other classic systems include the PROSPECTOR program for determining the probable location and type of ore deposits and the INTERNIST program for performing medical diagnosis in internal medicine.

THE FUTURE

A Report to ARPA on Twenty-First Century Intelligent Systems identified four types of systems that will have a substantial impact on applications: intelligent simulation, intelligent information resources, intelligent project coaches, and robot teams (Grosz and Davis, 1994).

Intelligent simulations generate realistic simulated worlds that enable extensive affordable training and education that can be made available any time and anywhere. Examples may be hurricane crisis management, exploration of the impacts of different economic theories, tests of products on simulated customers, and technological design—testing features through simulation that would cost millions of dollars to test using an actual prototype.

Intelligent information resources systems (IRSS) will enable easy access to information related to a specific problem. For instance, a rural doctor whose patient presents with a rare condition might use IRSS to help assess different treatments or identify new ones. An educator might find relevant background materials, including information about similar courses taught elsewhere.

Intelligent project coaches (IPC) could function as co-workers, assisting and collaborating with design or operations teams for complex systems. Such systems could remember and recall the rationale of previous decisions and, in times of crisis, explain the methods and reasoning previously used to handle that situation. An IPC for aircraft design, for example, could enhance collaboration by keeping communication flowing among the large, distributed design staff, the program managers, the customer, and the subcontractors.

Robot teams could contribute to manufacturing by operating in a dynamic environment with minimal instrumentation, thus providing the benefits of economies of scale. They could also participate in automating sophisticated laboratory procedures that require sensing, manipulation, planning, and transport.

CONCLUSION

AI is a young field and faces many complexities. Nonetheless, the Spring 1998 issue of AI Magazine contained articles on the following innovative applications of AI: This is suggestive of the broad potential of AI in the future.

1. "Case- and Constraint-Based Project Planning for Apartment Construction"
2. "CREWS–NS: Scheduling Train Crews in The Netherlands"
3. "An Intelligent System for Case Review and Risk Assessment in Social Services"
4. "CHEMREG: Using Case-Based Reasoning to Support Health and Safety Compliance in the Chemical Industry"
5. "MITA: An Information-Extraction Approach to the Analysis of Free-Form Text in Life Insurance Applications"