General Problem Solver

General Problem Solver (GPS) is an early AI program by Newell and Simon that models problem-solving by using means-ends analysis to reach a goal through smaller subgoals.

What is General Problem Solver?

General Problem Solver, or GPS, is one of the first classic AI programs in Intro to Cognitive Science. It was built by Allen Newell and Herbert A. Simon in the 1950s to model problem solving as a step-by-step process, not just as a mysterious flash of insight.

The core idea behind GPS is means-ends analysis. The program compares the current state to the goal state, spots the biggest difference, and then picks a move that reduces that difference. If the main goal is still too far away, GPS creates subgoals, which are smaller targets you can solve first. That makes the whole problem look more manageable.

A simple way to picture it is a puzzle. Instead of trying every possible move, GPS asks, "What is stopping me from reaching the goal right now?" Then it chooses an action that gets rid of one obstacle. If that action requires another step first, it builds a smaller subproblem and keeps going.

That is why GPS matters in cognitive science, not just in computer science. Newell and Simon were trying to show that human thinking could be described as information processing. In other words, problem solving might follow structured rules, use heuristics, and move through subgoals rather than happen as a single mental leap.

GPS was also limited. It worked best on problems that could be neatly described with clear goals and clear operations. Once a task required real-world knowledge, messy context, or deeper understanding, the program struggled. That weakness pushed cognitive scientists to think more carefully about knowledge representation, heuristics, and the difference between formal problem solving and human expertise.

Why General Problem Solver matters in Intro to Cognitive Science

GPS matters because it is one of the early bridges between psychology and artificial intelligence. In Intro to Cognitive Science, you are not just memorizing a historical program, you are seeing one of the first serious attempts to explain thinking with a computational model.

It shows a classic divide in the field: can intelligence be reduced to rules and search, or does human cognition need richer knowledge and context? GPS leaned hard toward the rule-based side. That makes it a good example when you are comparing symbolic AI to later approaches that rely more on learning, pattern recognition, or flexible memory systems.

GPS also gives you a concrete example of means-ends analysis, which shows up all over problem solving. When you break a task into subgoals, you are using the same basic structure Newell and Simon tried to model. That makes it useful for explaining how a thinker, or a program, moves from a hard goal to a series of doable steps.

Finally, GPS connects directly to later work in cognitive architectures and AI research. Even though the system was limited, it helped set the agenda for asking how people represent goals, search through possibilities, and choose among strategies.

How General Problem Solver connects across the course

Heuristic

GPS relied on heuristics, which are practical shortcuts for finding a solution without checking every possibility. In this model, the program does not search blindly. It uses a rule like means-ends analysis to pick a promising next step, which is exactly the kind of bounded problem solving cognitive science often studies.

Algorithm

An algorithm is a rule-based procedure for solving a problem, and GPS is built around that idea. The program follows a structured sequence for comparing the current state with the goal state, then choosing actions. The difference is that GPS is trying to model how a mind might solve problems, not just how a machine can.

Knowledge Representation

GPS depends on representing goals, states, and actions in a form the program can manipulate. If the system cannot encode the problem clearly, means-ends analysis falls apart. That is why knowledge representation became such a big issue in cognitive science and AI after early symbolic programs.

Soar

Soar is a later cognitive architecture that also grew out of the same problem-solving tradition as GPS. Both focus on goal-directed behavior and stepwise search, but Soar is more developed and flexible. Comparing them shows how cognitive models moved from early symbolic programs toward broader theories of mind.

Is General Problem Solver on the Intro to Cognitive Science exam?

A quiz question might ask you to identify GPS from a description of a program that solves problems by breaking them into subgoals. You may also need to explain means-ends analysis in your own words or compare GPS with a newer AI approach. If you get a short-answer prompt, the best move is to say what the program does, how it does it, and where it breaks down.

For essays or class discussion, use GPS as evidence that early cognitive science treated thinking like information processing. If the prompt asks why early AI matters, mention that GPS helped researchers model problem solving in a formal way, but also revealed the limits of purely rule-based systems when knowledge gets complex.

General Problem Solver vs Algorithm

People sometimes mix up GPS with an algorithm because GPS uses algorithmic steps. The difference is that GPS is a specific early AI program designed to model problem solving, while an algorithm is the general procedure it follows. GPS is one example of an algorithmic approach, not the same thing as the idea of an algorithm itself.

Key things to remember about General Problem Solver

• General Problem Solver is an early AI program from Newell and Simon that models human problem solving in a formal way.

• Its main strategy is means-ends analysis, which means comparing your current situation with a goal and then creating subgoals to close the gap.

• GPS is a classic symbolic AI example because it treats thinking as rule-based search over clearly defined states and actions.

• The program works best when the problem is structured, but it struggles when a task needs lots of real-world knowledge or flexible reasoning.

• In Intro to Cognitive Science, GPS is a key example of how psychologists and computer scientists tried to explain the mind with computational models.

Frequently asked questions about General Problem Solver

What is General Problem Solver in Intro to Cognitive Science?

General Problem Solver is an early artificial intelligence program that tries to solve problems by using means-ends analysis. In Intro to Cognitive Science, it matters because it shows how researchers modeled thinking as a stepwise process with goals, subgoals, and rules.

How does General Problem Solver use means-ends analysis?

It compares the current state to the desired goal state, then looks for the biggest difference between them. GPS picks an action that reduces that difference, and if needed, it creates smaller subgoals to make the main goal reachable.

Why did General Problem Solver have limitations?

GPS worked best when problems were cleanly defined, with clear rules and clear possible moves. It struggled when a task depended on deeper background knowledge, context, or messy real-world reasoning, which is a big reason later AI systems took different approaches.

Is General Problem Solver the same as a heuristic?

No. GPS is a program, while a heuristic is a shortcut or strategy used inside problem solving. GPS uses heuristics, especially means-ends analysis, to decide which smaller step to try next.