目录

1 Introduction

  • symbolic theory
  • explain, and unity
  • causal language, \(Datalog^\ni\), generates a \(Datalog^\ni\) program
  • relatively human-readable
  • data-efficient
  • elementary cellular automata, music, Seek Whence (sequences), multi-modal binding, occlusion
  • model-based RL or MCTS
    • accurate model of the game dynamics
  • learning models
  • three dimensions: latent? symbolic? prior?
    • HMM
    • only transition
    • transition, perception, render
    • ours: latent states, latent objects
    • vectors: hard. symbols: relatively easy to understand
    • some: state symbols, transition tensors
    • prior: conv? event calculus? rules?

2 Background

  • Datalog clause, interpreter in ASP
  • subset-minimal Herbrand model
  • ASP solvers, weak constraints

3 A computational framework for making sense of sensory sequences

3.1 - 3.4

  • unambiguous symbolic sensory sequence
  • theory, type, initial conditions, rules, constraints
    • static rule, causal rule
    • unary, binary, uniqueness constraint
    • disallowing constants
  • constraint, incompossible
  • covered by
  • example: three cycled states
  • unity
  • cost

3.5 -

  • different interpretations
  • trivial interpretation, upper bound

4 Computer implementation

  • template, type signature, constants (static, causal, body atoms)
  • increasing complexity
  • lowest cost
  • two non-trivial parts
    • enumerate templates
    • diagonalization, \((T,n)\) pairs
    • infinite list of finite lists of: objects, predicates, variables
  • find the best theory
    • deduction, abduction, induction, combine (facts, rules, outputs)
    • \(datalog^\ni\) interpreter in ASP
  • ASP encoding, meta-interpreter
  • complexity
  • optimization

6 Noisy apperception

  • length: increasing performance
  • percentage of mislabelled data: decreasing performance