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When problem-solving fails, querying the expert to either learn a new exemplar for problem-solving or to learn a new explanation as to exactly why one exemplar is more relevant than another. For example, the program Protos learned to diagnose tinnitus cases by interacting with an audiologist.
Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm. In the past, problems supposed to be AI-complete included computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. [2] AI-complete were notably considered useful for ...
Means–ends analysis [1] (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs.. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods.
Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent , autonomous entities that can communicate with each other, was developed to serve as an ...
The Riemann hypothesis catastrophe thought experiment provides one example of instrumental convergence. Marvin Minsky, the co-founder of MIT's AI laboratory, suggested that an artificial intelligence designed to solve the Riemann hypothesis might decide to take over all of Earth's resources to build supercomputers to help achieve its goal. [2]
The study of multi-agent systems is "concerned with the development and analysis of sophisticated AI problem-solving and control architectures for both single-agent and multiple-agent systems." [17] Research topics include: agent-oriented software engineering; beliefs, desires, and intentions ; cooperation and coordination