ABSTRACT
The success of game playing has led scientists in other fields to consider its artificial intelligence (AI) algorithms an accurate representation of real-world problems. In the mid 1970s, the paradigm "knowledge is more important than algorithms" generated the first relatively successful AI system; it embodied expert knowledge in restricted domains like blood infections or mineral sites. The main directions of AI research and development include: specific hard and soft environment development for AI; deep knowledge in intelligent, knowledge-based systems; the integration and cooperation of intelligent, knowledge-based systems; and learning algorithms. Three different models support AI languages as privileged vehicles for knowledge, heuristics, and inference: functional languages like Lisp; object-oriented programs like Small Talk; and logic programming like Prolog. An important set of techniques and methodologies in AI deals with search problems. Search is the process of navigation among states that represent possible problem situations.
