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Cognitive Architectures
Published in Ron Fulbright, Democratization of Expertise, 2020
As shown in Fig. 6-1, one of the earliest efforts in artificial intelligence research, the General Problem Solver (GPS) was created in 1959 and was intended to be a universal problem-solver machine (Newell et al., 1959). At a given point in time, the machine exists in any of a set of states called the problem state space with one state declared the goal state as shown in Fig. 6-1. In each iteration, the machine determines the distance from the current state to the goal state and then selects an operator to perform resulting in the machine moving to a state closer to the goal. The machine iterates until the goal is achieved. GPS was the first computer program separating the knowledge of problems from the strategy of how to solve problems. While it was a necessary first logical step, GPS was never able to solve real-world problems. However, GPS did evolve eventually into the Soar architecture described later.
Introduction
Published in C.S. Krishnamoorthy, S. Rajeev, Artificial Intelligence and Expert Systems for Artificial Intelligence Engineers, 2018
C.S. Krishnamoorthy, S. Rajeev
Newell, Shaw and Simon developed a program called General Problem Solver (GPS) in 1959, that could solve many types of problems. It was capable of proving theorems, playing chess and solving complex puzzles. GPS introduced the concept of means-end analysis, involving the matching of present state and goal state. The difference between the two states was used to find out new search directions. GPS also introduced the concept of backtracking and subgoal states that improved the efficiency of problem solving [3]. Backtracking is used when the search drifts away from the goal state from a previous nearer state, to reach that state. The concept of subgoals introduced a goal-driven search through the knowledge. The major criticism of GPS was that it could not learn from previously solved problems. In the same year, John McCarthy developed LISP programming language, which became the most widely used AI programming language [4].
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
In the early days of development, researchers pursued the goal of building general problem solvers like the Logic Theorist and GPS (General Problem Solver), (Newell and Simon, [18,19]). Progress in that direction stalled, the developed systems did not find widespread applicability and were unable to provide complete solutions to a large variety of complex problems. As a result a change of focus took place. It was realized that specialized problem solving knowledge could be developed and applied to the various domains, instead of searching for an omnipotent, general purpose, problem solving methodology. Specialized problem solvers and techniques started being developed to solve particular problems. The advent of Expert or Knowledge Based Systems is a result of pursuing this goal of constructing specialized problem solvers.
Design spaces and EEG frequency band power in constrained and open design
Published in International Journal of Design Creativity and Innovation, 2022
Sónia Vieira, Mathias Benedek, John Gero, Shumin Li, Gaetano Cascini
The notion of design spaces has its origin in the formation of the problem space and has been a subject of investigation and debate for the last 60 years. In the problem space theory of problem-solving (Newell & Simon, 1972), new constraints, subgoals, and design alternatives evoked from long-term memory leading to shifts in external memory representations, such as models and drawings, would be considered as changes of the problem space. The problem solver retrieval system (General Problem Solver, Newell et al., 1959), whether a human or computer, would continually modify and characterize the problem space while searching for solutions. The use of methods and techniques available for tackling ill-structured problems (Simon, 1973) would vary within the extent of the designer’s limited capacities and according to the problem’s goals, constraints, and generated alternatives. An alternative approach to problem-solving later emerged as a reflective practice (Schön, 1983, 1987). The designer, by thinking and doing (knowing in action), would construct the design world and set the dimensions of the problem space and the moves by which he/she would attempt to find solutions (Argyris et al., 1985; Schön, 1992). The situated cognition research approach (Clancey, 1997) elaborated the idea that learning takes place when an individual is doing something. The term situated emphasized that perceptual mechanisms causally relate human cognition to the environment and action. Being situated involved a causal coupling in the moment within internal organizing and between internal and external organizing, while changing things in the world. As a research approach, situated cognition was suitable to investigate design cognition (Gero, 1990). Design, seen as a temporal activity that generates appropriate solutions to situated and open requests framing the designer’s mental space, would require constructs such as problem finding (Simon, 1995) before problem-solving takes place (Runco, 1994; Runco & Nemiro, 1994). In the last 40 years, alternative views to the problem-solving space emerged with the focus on the ultimate purpose for change, the solution space.