AI & Cognitive Science Reasoning Methods 100+

AI & Cognitive Science Reasoning Methods 100+

I. Probabilistic and Statistical Reasoning

  1. Bayesian Inference
  2. Bayesian Networks
  3. Dynamic Bayesian Networks
  4. Hidden Markov Models (HMMs)
  5. Markov Random Fields / Markov Networks
  6. Monte Carlo Methods (MCMC, Importance Sampling)
  7. Variational Inference
  8. Probabilistic Programming
  9. Gaussian Processes for reasoning under uncertainty
  10. Bayesian Occam’s Razor / Model Selection
  11. Probabilistic Soft Logic
  12. Probabilistic Relational Models
  13. Hierarchical Bayesian Models
  14. Particle Filtering (Sequential Monte Carlo)
  15. Expectation-Maximization (EM) for latent variable models
  16. Deep Probabilistic Models (e.g., Variational Autoencoders)
  17. Statistical Relational Learning
  18. Probabilistic Logic Networks

II. Non-Monotonic and Defeasible Reasoning

  1. Defeasible Logic
  2. Default Logic
  3. Circumscription
  4. Belief Revision (AGM Theory)
  5. Autoepistemic Logic
  6. Argumentation Frameworks (Dung’s Theory)
  7. Prioritized Default Reasoning
  8. Non-Monotonic Description Logics
  9. Preference-Based Reasoning
  10. Defeasible Argumentation in AI
  11. Formal Argumentation for Dialogue Systems
  12. Dynamic Epistemic Logic

III. Abductive and Explanatory Reasoning

  1. Abductive Logic Programming
  2. Explanation-Based Learning (EBL)
  3. Hypothesis Generation and Ranking
  4. Minimal Explanation Principle
  5. Diagnostic Reasoning Models
  6. Counterfactual Abduction
  7. Bayesian Abductive Reasoning
  8. Model-Based Diagnosis
  9. Plan Recognition via Abduction
  10. Causal Abduction Models

IV. Analogical and Similarity-Based Reasoning

  1. Structure-Mapping Theory
  2. Case-Based Reasoning (CBR)
  3. Analogical Problem Solving
  4. Conceptual Blending
  5. Embedding-Based Similarity Reasoning
  6. Vector Space Models for Analogy
  7. Prototype and Exemplar Reasoning
  8. Relational Similarity Computation
  9. Graph Matching for Analogy
  10. Metaphor and Analogy in NLP

V. Causal and Counterfactual Reasoning

  1. Structural Causal Models (SCMs)
  2. Pearl’s Do-Calculus for Interventions
  3. Counterfactual Inference
  4. Causal Discovery Algorithms
  5. Granger Causality
  6. Causal Bayesian Networks
  7. Causal Mediation Analysis
  8. Temporal Causal Models
  9. Probabilistic Causation Models
  10. Learning Causal Relations from Observational Data

VI. Commonsense and Heuristic Reasoning

  1. Script-Based Reasoning
  2. Frame-Based Reasoning
  3. ConceptNet and Commonsense Knowledge Graphs
  4. Heuristic Search Algorithms (A*, Beam Search)
  5. Fast and Frugal Trees
  6. Bounded Rationality Models
  7. Dual Process Models (System 1 and 2 Reasoning)
  8. Intuitive Physics Engines
  9. Mental Simulation for Reasoning
  10. Prototype-Based Categorization

VII. Symbolic, Knowledge-Based, and Ontological Reasoning

  1. Ontology-Based Reasoning
  2. Description Logics (DL)
  3. Semantic Web Reasoning (OWL, RDF)
  4. Production Rule Systems
  5. Logic Programming (Prolog, ASP)
  6. Knowledge Graph Reasoning
  7. Conceptual Graphs
  8. Constraint Satisfaction Problems (CSP)
  9. Automated Theorem Proving
  10. Deductive Databases

VIII. Neural, Deep Learning, and Representation-Based Reasoning

  1. Transformer Attention-Based Reasoning
  2. Chain-of-Thought (CoT) Prompting
  3. Tree-of-Thought (ToT) Reasoning
  4. Graph Neural Networks (GNNs)
  5. Neuro-Symbolic Reasoning
  6. Deep Reinforcement Learning Reasoning
  7. Latent Space Reasoning
  8. Contrastive Reasoning with Embeddings
  9. Multimodal Reasoning Models
  10. Self-Consistency Sampling in LLMs

IX. Hybrid and Neuro-Symbolic Approaches

  1. Logic Tensor Networks (LTNs)
  2. DeepProbLog (Deep Probabilistic Logic Programming)
  3. Neural Theorem Provers
  4. Differentiable Programming with Logical Constraints
  5. Symbolic Regression via Neural Networks
  6. Neuro-Symbolic Relational Learning
  7. Symbolic Planning with Neural Guidance
  8. Neuro-Symbolic Concept Learners
  9. Differentiable Answer Set Programming
  10. Hybrid Logical Neural Architectures

X. Decision-Theoretic and Reinforcement Reasoning

  1. Markov Decision Processes (MDPs)
  2. Partially Observable MDPs (POMDPs)
  3. Policy Gradient Methods
  4. Model-Based Reinforcement Learning
  5. Inverse Reinforcement Learning
  6. Reinforcement Learning with Human Feedback (RLHF)
  7. Multi-agent Reinforcement Learning
  8. Reward Shaping
  9. Q-Learning with Explanation Traces
  10. Game-Theoretic Reasoning

XI. Meta-Reasoning, Self-Reflective, and Autonomous Reasoning

  1. Meta-Chain-of-Thought Reasoning
  2. Self-Reflective Agent Architectures
  3. Debate-Based Reasoning Frameworks
  4. Self-Verification and Auto-Critique Models
  5. Reasoning with Extended Context and Memory
  6. Reasoning with External Knowledge Stores
  7. Self-Improving Reasoning via Reflection
  8. Adaptive Reasoning Step Models
  9. Contrastive Chain-of-Thought Reasoning
  10. Recursive Reasoning Architectures

XII. Cutting-Edge and Novel Reasoning Frameworks

  1. Tree-of-Thought Search Algorithms
  2. Graph-of-Thought Reasoning
  3. Function-Calling Language Models
  4. Planning-Driven Language Models
  5. Multi-Agent Collaborative Reasoning
  6. Sparse Attention Reasoning Models
  7. Memory-Compressed Reasoning Transformers
  8. Gradient-Logic Hybrid Networks
  9. Value Learning for Moral Reasoning
  10. Debate and Voting Models for AI Alignment

Summary:
This list captures 130+ distinct reasoning methods and frameworks prominent in Extended AI and Cognitive Science, covering statistical, symbolic, neural, hybrid, meta, decision-theoretic, and emerging methods.

Each method is rigorously studied, applied in domains like natural language processing, robotics, cognitive modeling, knowledge representation, and AI alignment.