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Technical description of CogPrime cognitive architecture and SRAM modelAcademic discussion of formal definitions of general intelligence
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kaggle-ho-013050House Oversight

Technical exposition on formal models of intelligent agents (CogPrime/PLN)

Technical exposition on formal models of intelligent agents (CogPrime/PLN) The passage is a purely academic description of AI formalism with no mention of political figures, financial transactions, or misconduct. It offers no actionable investigative leads. Key insights: Describes a formal goal‑execution model using holds(C) and ex(P).; References CogPrime notation and Predictive Extension Implication relations.; Applies the model to a virtual dog scenario.

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Technical exposition on formal models of intelligent agents (CogPrime/PLN) The passage is a purely academic description of AI formalism with no mention of political figures, financial transactions, or misconduct. It offers no actionable investigative leads. Key insights: Describes a formal goal‑execution model using holds(C) and ex(P).; References CogPrime notation and Predictive Extension Implication relations.; Applies the model to a virtual dog scenario.

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“olds(C) & ex(P) > h, where h may be an externally specified goal g; or an internally specified goal h derived”

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kagglehouse-oversightartificial-intelligencecognitive-architectureformal-modeling
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134 7 A Formal Model of Intelligent Agents Context & Procedure — Goal and considered more formally as holds(C) & ex(P) > h, where h may be an externally specified goal g; or an internally specified goal h derived as a (possibly uncertain) subgoal of one of more gi; C is a piece of declarative or episodic knowledge and P is a procedure that the agent can internally execute to generate a series of actions. ex(P) is the proposition that P is successfully executed. If C is episodic then holds(C) may be interpreted as the current context (i.e. some finite slice of the agent’s history) being similar to C; if C is declarative then holds(C) may be interpreted as the truth value of C' evaluated at the current context. Note that C may refer to some part of the world quite distant from the agent’s current sensory observations; but it may still be formally evaluated based on the agent’s history. In the standard CogPrime notation as introduced formally in Chapter 20 (where indentation has function-argument syntax similar to that in Python, and relationship types are prepended to their relata without parentheses), for the case C is declarative this would be written as PredictiveExtensionallmplication AND C Execution P G and in the case C' is episodic one replaces C in this formula with a predicate expressing C’s similarity to the current context. The semantics of the PredictiveExtensionalInheritance relation will be discussed below. The Execution relation simply denotes the proposition that procedure P has been executed. For the class of SRAM agents who (like CogPrime) use the cognitive schematic to govern many or all of their actions, a significant fragment of agent intelligence boils down to estimating the truth values of PredictiveExtensionallmplication relationships. Action selection procedures can be used, which choose procedures to enact based on which ones are judged most likely to achieve the current external goals g; in the current context. Rather than enter into the particularities of action selection or other cognitive architecture issues, we will restrict ourselves to PLN inference, which in the context of the present agent model is a method for handling Predictivelmplication in the cognitive schematic. Consider an agent in a virtual world, such as a virtual dog, one of whose external goals is to please its owner. Suppose its owner has asked it to find a cat, and it can translate this into a subgoal “find cat.” If the agent operates according to the cognitive schematic, it will search for P so that PredictiveExtensionallmplication AND C Execution P Evaluation found cat holds.

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