Definition
General Problem Solving is the cognitive ability to identify, analyze, and solve problems across diverse domains using flexible reasoning strategies and adaptable approaches. Unlike specialized problem-solving methods designed for specific tasks, general problem solving involves the capacity to tackle any type of intellectual challenge by applying fundamental cognitive processes and learning from experience.
This capability represents a core component of Artificial General Intelligence, enabling systems to:
- Adapt to new situations without prior specific training
- Transfer knowledge from one domain to another
- Generate novel solutions through creative reasoning
- Learn from failures and improve strategies over time
- Apply multiple approaches to complex problems
How It Works
General problem solving operates through a systematic process that combines multiple cognitive capabilities and can be enhanced through Machine Learning and Neural Networks.
Problem-Solving Framework
The systematic approach to tackling any problem
- Problem Recognition: Identifying that a problem exists and understanding its nature
- Problem Representation: Converting the problem into a format that can be analyzed using Information Retrieval and Semantic Search
- Strategy Selection: Choosing appropriate solution methods from available approaches
- Solution Execution: Implementing the chosen strategy with Optimization techniques
- Evaluation: Assessing the effectiveness of the solution
- Adaptation: Learning from the process to improve future problem-solving
Core Cognitive Processes
Fundamental mental operations that enable general problem solving
- Pattern Recognition: Identifying similarities and regularities across different problems using Deep Learning and Computer Vision
- Abstract Reasoning: Working with concepts and principles beyond concrete examples through Natural Language Processing
- Logical Thinking: Applying deductive and inductive reasoning with Decision Trees and Classification
- Creative Thinking: Generating novel approaches and solutions using Generative AI
- Meta-cognition: Thinking about thinking and monitoring one's own problem-solving process
Types
Problem-Solving Strategies
Algorithmic Approaches
- Systematic Search: Methodically exploring solution spaces using Vector Search and Optimization
- Example: Finding the shortest path in a network by exploring all possible routes
- Divide and Conquer: Breaking complex problems into manageable sub-problems
- Example: Solving a large puzzle by working on sections separately
- Working Backwards: Starting from the desired outcome and reasoning backwards
- Example: Planning a project by starting with the deadline and working backwards
- Pattern Matching: Identifying similarities to previously solved problems
- Example: Recognizing that a new math problem follows the same pattern as a solved one
Heuristic Methods
- Rule of Thumb: Using general principles and guidelines
- Example: "If it's too good to be true, it probably is" for evaluating opportunities
- Analogy: Drawing parallels with similar problems or situations
- Example: Understanding electricity by comparing it to water flow
- Trial and Error: Testing different approaches systematically
- Example: Trying different cooking temperatures to perfect a recipe
- Intuition: Using subconscious pattern recognition and experience
- Example: A doctor's "gut feeling" about a diagnosis based on years of practice
Creative Approaches
- Lateral Thinking: Looking at problems from unexpected angles
- Example: Solving traffic congestion by encouraging remote work instead of building more roads
- Brainstorming: Generating multiple possible solutions
- Example: Team meeting to generate 50 different marketing campaign ideas
- Reframing: Restating the problem in different terms
- Example: Viewing "customer complaints" as "feedback opportunities"
- Synthesis: Combining elements from different domains
- Example: Creating a new product by combining features from unrelated industries
Problem Categories
Well-Structured Problems
- Clear goals and constraints with defined solution spaces
- Systematic solution methods using Supervised Learning
- Predictable outcomes and evaluation criteria
- Examples: Mathematical equations, algorithmic challenges, logical puzzles
Ill-Structured Problems
- Ambiguous goals or constraints requiring interpretation
- Multiple valid solutions with different trade-offs
- Uncertain outcomes and evaluation criteria
- Examples: Design challenges, policy decisions, creative projects
Complex Adaptive Problems
- Dynamic constraints that change during problem-solving
- Emergent properties that arise from interactions
- Feedback loops that affect future decisions
- Examples: Ecosystem management, organizational change, social systems
Real-World Applications
Scientific Research & Discovery
- Hypothesis generation: Creating testable theories across disciplines using Machine Learning
- Experimental design: Planning research studies and data collection strategies
- Data analysis: Interpreting complex datasets with Time Series and Clustering
- Cross-disciplinary insights: Connecting knowledge from different fields through Information Retrieval
Business & Strategy
- Strategic planning: Developing long-term business strategies and competitive advantages
- Process optimization: Improving efficiency and effectiveness across operations with Optimization
- Innovation management: Creating new products, services, and business models
- Risk assessment: Identifying and mitigating potential threats and opportunities
Technology Development
- Software architecture: Designing complex systems and applications
- Algorithm design: Creating efficient computational solutions
- User experience design: Solving interface and interaction challenges
- System integration: Connecting diverse technologies and platforms
Current AI Applications (2025)
- Large Language Models: GPT-5, Claude Sonnet 4, and Gemini 2.5 solving diverse reasoning tasks
- Code Generation: GitHub Copilot and similar tools solving programming problems
- Scientific Discovery: AI systems like AlphaFold solving protein structure problems
- Game Playing: Systems like AlphaGo and AlphaZero mastering complex games
- Robotics: Autonomous robots solving physical manipulation and navigation problems
- Creative AI: Systems generating art, music, and literature through creative problem solving
Key Concepts
Fundamental principles that underlie effective general problem solving
Cognitive Flexibility
- Adaptive thinking: Switching between different problem-solving approaches as needed
- Perspective taking: Viewing problems from multiple angles and viewpoints
- Strategy selection: Choosing the most appropriate method for each situation
- Learning transfer: Applying knowledge from one domain to another
Systematic Approach
- Structured thinking: Following organized methods and frameworks
- Documentation: Recording problem-solving processes and outcomes
- Iteration: Refining solutions through multiple cycles of improvement
- Validation: Testing solutions against criteria and constraints
Creative Problem Solving
- Divergent thinking: Generating multiple possible solutions and approaches
- Convergent thinking: Evaluating and selecting the best solutions
- Innovation: Creating novel approaches and breakthrough solutions
- Synthesis: Combining ideas from different sources and domains
Meta-Cognitive Skills
- Self-awareness: Understanding one's own thinking processes and biases
- Strategy monitoring: Tracking the effectiveness of problem-solving approaches
- Learning reflection: Analyzing what worked and what didn't
- Continuous improvement: Developing better problem-solving capabilities over time