Free Artificial Intelligence Article – Ebook

June 4, 2006 · Posted in Computing 

Free Artificial Intelligence Article – Ebook
Artificial Intelligence is “the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines.” It is the science and engineering of making intelligent machines, especially intelligent computer programs.
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

Introduction

Artificial intelligence (AI) is a branch of computer science that studies the computational requirements for tasks such as perception, reasoning, and learning, and develops systems to perform those tasks. AI is a diverse field whose researchers address a wide range of problems, use a variety of methods, and pursue a spectrum of scientific goals. For example, some researchers study the requirements for expert performance at specialized tasks, while others model commonsense processes; some researchers explain behaviors in terms of low-level processes, using models inspired by the computation of the brain, while others explain them in terms of higher-level psychological constructs such as plans and goals. Some researchers aim to advance understanding of human cognition, some to understand the requirements for intelligence in general (whether in humans or machines), and some to develop artifacts such as intelligent devices, autonomous agents, and systems that cooperate with people to amplify human abilities.

AI is a young field–even its name, “artificial intelligence,” was only coined in 1956. One of the challenges for AI has been to determine which tasks to study–what constitutes an “AI question”–and how to evaluate progress. Much early AI research focused on tasks commonly thought to require high intelligence in people, such as playing high-quality chess. Skeptics viewed this as an impossible assignment, but AI made rapid progress. By the 1960’s, programs were capable of tournament play. In 1997, in a landmark match, the chess system Deep Blue defeated Gary Kasparov, the world’s human chess champion for the previous twelve years. At the same time, however, AI research was illuminating the enormous difficulty of commonsense tasks that people take for granted, such as understanding stories or conversations. Developing programs that can deal at a human level with rich everyday reasoning remains a fundamental research challenge

Knowledge capture, representation and reasoning
In the logicist approach to knowledge representation and reasoning, information is encoded as assertions in a logic, and the system draws conclusions by deduction from those assertions . Other research studies non-deductive forms of reasoning, such as reasoning by analogy and abductive inference–the process of inferring the best explanation for a set of facts. Abductive inference does not guarantee sound conclusions, but is enormously useful for tasks such as medical diagnosis, in which a reasoner must hypothesize causes for a set of symptoms.

Capturing the knowledge needed by AI systems has proven to be a challenging task. The knowledge in rule-based expert systems, for example, is represented in the form of rules listing conditions to check for, and conclusions to be drawn if those conditions are satisfied. For example, a rule might state that IF certain conditions hold (e.g., the patient has certain symptoms), THEN certain conclusions should be drawn (e.g., that the patient has a particular condition or disease). A natural way to generate these rules is to interview experts. Unfortunately, the experts may not be able to adequately explain their decisions in a rule-based way, resulting in a “knowledge-acquisition bottleneck” impeding system development

Conclusion and Resources

In its short existence, AI has increased understanding of the nature of intelligence and provided an impressive array of applications in a wide range of areas. It has sharpened understanding of human reasoning, and of the nature of intelligence in general. At the same time, it has revealed the complexity of modeling human reasoning, providing new areas and rich challenges for the future.


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