In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills.The approach was originally proposed as an alternative to traditional approaches (that is, approaches popular before 1985 or so). After several dec fact, classical AI technologies face intractable issues, such as combination explosion, when confronted with real-world modeling problems, and several directions have been explored by researchers to address these issues. All these approaches focus on modeling intelligence situated in a given environment: they have come to be known as the situated approach to AI.
Abstract
In the domain of intelligent systems, the management of mental resources is typically called “attention”. Attention exists because all moderately complex environments – and the real-world environments of everyday life in particular – are a source of vastly greater information than can be processed in real-time by available cognitive resources of any known intelligence, human or otherwise. General-purpose artificial intelligence (AI) systems operating with limited resources under time-constraints in such environments must select carefully which information will be processed and which will be ignored. Even in the (rare) cases where sufficient resources may be available, attention could help make better use of them. All real-world tasks come with time limits, and managing these is a key part of the role of intelligence. Many AI researchers ignore this fact. As a result, the majority of existing AI architectures is incorrectly based on an (explicit or implicit) assumption of infinite or sufficient computational resources. Attention has not yet been recognized as a key cognitive process of AI systems and in particular not of artificial general intelligence systems. This dissertation argues for the absolute necessity of an attention mechanism for artificial general intelligence (AGI) architectures. We examine several issues related to attention and resource management, review prior work on these topics in cognitive psychology and AI, and present a design for a general attention mechanism for AGI systems. The proposed design – inspired by constructivist AI methodologies – aims at architectural and modal independence, and comprehensively addresses and integrates all principal factors associated with attention to date.