Artificial Intelligence
Knowledge and Control for a Mechanical Design Expert System
Computer - Special issue on expert systems in engineering
SOAR: an architecture for general intelligence
Artificial Intelligence
International Journal of Man-Machine Studies - Knowledge acquisition for knowledge-based systems. Part 2
Design problem solving: a task analysis
AI Magazine
Situated cognition: Stepping out of representational flatland
AI Communications
Task-structure analysis for knowledge modeling
Communications of the ACM - Special issue on analysis and modeling in software development
Generic tasks and task structures: history, critique and new directions
Second generation expert systems
Explanation using task structure and domain functional models
Second generation expert systems
Task-specific architectures for flexible systems
The Soar papers (vol. II)
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Explaining Control Strategies in Problem Solving
IEEE Expert: Intelligent Systems and Their Applications
Viewing Knowledge Bases as Qualitative Models
IEEE Expert: Intelligent Systems and Their Applications
Task-Structures, Knowledge Acquisition and Learning
Machine Learning
Multimodal cognitive architecture: making perception more central to intelligent behavior
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Software-engineering challenges of building and deploying reusable problem solvers
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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I was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support the construction of knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.