Ch1 AI: History and Applications

Ch1 AI: History and Applications Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2011

Outline • AI History • Overview of AI application areas

History • There are two consequences of mind/body analysis essential to the AI enterprise: • Mental processes have an existence of their own, obey their own laws, and can be studied in and of themselves • Once the mind and the body are separated, philosophers found it necessary to find a way to reconnect the two

History: AI and the Rationalist • Modern research issues in AI are formed and evolve through a combination of historical, social and cultural pressures. • The rationalist tradition had an early proponent in Plato, and was continued on through the writings of Pascal, Descates, and Liebniz • For the rationalist, the external world is reconstructed through the clear and distinct ideas of a mathematics

History: Development of Formal Logic • The goal of creating a formal language for thought also appears in the work of George Boole, another 19th century mathematician whose work must be included in the roots of AI • The importance of Boole’s accomplishment is in the extraordinary power and simplicity of the system he devised: Three Operations

History: the Turning Test • The imitation game (1950)

Outline • AI History • Overview of AI application areas

AI application areas • Game Playing • Much of the early research in state space search was done using common board games such as checkers, chess, and the 15-puzzle • Games can generate extremely large search spaces. Theses are large and complex enough to require powerful techniques for determining what alternative to explore

AI application areas • Automated reasoning and Theorem Proving • Theorem-proving is one of the most fruitful branches of the field • Theorem-proving research was responsible in formalizing search algorithms and developing formal representation languages such as predicate calculus and the logic programming language

AI application areas • Expert System • One major insight gained from early work in problem solving was the importance of domain-specific knowledge • Expert knowledge is a combination of a theoretical understanding of the problem and a collection of heuristic problem-solving rules

AI application areas • Expert System • Current deficiencies: • Lack of flexibility; if human cannot answer a question immediately, he can return to an examination of first principle and come up something • Inability to provide deep explanations • Little learning from experience

AI application areas • Natural Language Understanding and Semantics • One of the long-standing goals of AI is the creation of programming that are capable of understanding and generating human language

AI application areas • Modeling Human Performance • Capture the human mind (knowledge representation)

AI application areas • Robotics • A robot that blindly performs a sequence of actions without responding to changes or being able to detect and correct errors could hardly considered intelligent • It should have some degree of sensors and algorithms to guild it

AI application areas • Machine Learning • Learning has remained a challenging area in AI • An expert system may perform extensive and costly computation to solve a problem; unlike human, it usually don’t remember the solution

Decision Tree Example

AI application areas • Alternative representations • Neural Networks • Genetic Algorithm

Ch1 AI: History and Applications