World Model System

A World Model System is a representational system that can be implemented by a world model agent to maintain environmental representations (for predictive reasoning and action planning).

• AKA: Environmental Model, Internal World Representation, Predictive Model System, Mental Model System.
• Context:
  • It can typically construct State Representations through world model observation processing.
  • It can typically predict Future States through world model forward simulation.
  • It can typically maintain Object Permanence through world model memory systems.
  • It can typically support Counterfactual Reasoning through world model alternative pathways.
  • It can typically enable Planning Processes through world model action evaluation.
  • ...
  • It can often incorporate Uncertainty Modeling through world model probabilistic representation.
  • It can often update Belief States through world model evidence integration.
  • It can often compress Sensory Information through world model abstraction layers.
  • It can often facilitate Transfer Learning through world model generalization mechanisms.
  • ...
  • It can range from being a Simple World Model System to being a Complex World Model System, depending on its world model representational complexity.
  • It can range from being a Discrete World Model System to being a Continuous World Model System, depending on its world model state space.
  • It can range from being a Deterministic World Model System to being a Stochastic World Model System, depending on its world model uncertainty handling.
  • It can range from being a Static World Model System to being a Dynamic World Model System, depending on its world model temporal evolution.
  • It can range from being a Observable World Model System to being a Partially-Observable World Model System, depending on its world model information completeness.
  • ...
  • It can integrate with Planning Systems for action sequence generation.
  • It can connect to Perception Systems for sensory input processing.
  • It can interface with Learning Algorithms for model improvement.
  • It can communicate with Decision Systems for policy optimization.
  • It can synchronize with Memory Systems for experience retention.
  • ...
• Example(s):
  • Neural World Model Systems, such as:
    • Dreamer Models, implementing world model-based reinforcement learning with latent imagination.
    • World Models (Ha & Schmidhuber), combining variational autoencoders with recurrent neural networks.
    • MuZero Model, learning world model dynamics without environmental knowledge.
  • Cognitive World Model Systems, such as:
    • Human Mental Models, maintaining cognitive maps for spatial navigation.
    • Theory of Mind Models, representing other agent beliefs for social reasoning.
  • Robotic World Model Systems, such as:
    • SLAM Systems, building simultaneous localization with mapping representation.
    • Occupancy Grid Models, maintaining spatial probability maps for navigation planning.
  • Game AI World Model Systems, such as:
    • Chess Position Evaluators, representing board states for move planning.
    • StarCraft II Models, tracking game states for strategic decision.
  • Language World Model Systems, such as:
    • Situation Models, maintaining narrative states in text understanding.
    • Discourse Representations, tracking entity relations in dialogue systems.
  • ...
• Counter-Example(s):
  • Reactive Systems, which respond to immediate stimulus without internal representation.
  • Lookup Tables, which store fixed mappings without predictive capability.
  • Stateless Systems, which process current input without environmental model.
• See: System Model, Internal Representation, Planning System, Reinforcement Learning Algorithm, Cognitive Map, Mental Model, Predictive Model, Agent Architecture.

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