1.12 Key Claims of the Book 15 e inter-reflecting networks modeling self and others, reflecting a “mirrorhouse” design pattern 10. Given the strengths and weaknesses of current and near-future digital computers, a. A (loosely) neural-symbolic network is a good representation for directly storing many kinds of memory, and interfacing between those that it doesn’t store directly; b. Uncertain logic is a good way to handle declarative knowledge. To deal with the prob- lems facing a human-level AGI, an uncertain logic must integrate imprecise probability and fuzziness with a broad scope of logical constructs. PLN is one good realization. c. Programs are a good way to represent procedures (both cognitive and physical-action, but perhaps not including low-level motor-control procedures). d. Evolutionary program learning is a good way to handle difficult program learning prob- lems. Probabilistic learning on normalized programs is one effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reasonable way to handle atten- tional knowledge (though other approaches, with greater overhead cost, may provide better accuracy and may be appropriate in some situations). e Artificial economics is an effective approach to activation spreading and Hebbian learning in the context of neural-symbolic networks; e ECAN is one good realization of artificial economics; e A good trade-off between comprehensiveness and efficiency is to focus on two kinds of attention: processor attention (represented in CogPrime by ShortTermImpor- tance) and memory attention (represented in CogPrime by LongTermImportance). g. Simulation is a good way to handle episodic knowledge (remembered and imagined). Running an internal world simulation engine is an effective w