• 1 Introduction
  • 1.1 Related work
• 2 Background
• 3 A computational framework for making sense of sensory sequences
  • 3.1 - 3.4
  • 3.5 -
• 4 Computer implementation
• 6 Noisy apperception

• AI 2021
• https://www.sciencedirect.com/science/article/pii/S0004370220301855
• an ILP system for sequences. predict, retrodict, impute
• unsupervised program synthesis
• PI, object invention
• noised but low-dim inputs with only a few types of labels

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

1.1 Related work

• 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

标签：ASP,theory,AI,transition,sensory,rules,interpreter,input
来源： https://www.cnblogs.com/minor-second/p/15521913.html