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HybridAgentGPTinstructFit.txt
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HybridAgentGPTinstructFit.txt
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Input=get_input(); Thoughts=Hybrid_ThoughtGen(Input, Context); QualityScores=StateEval(Thoughts, Metrics); Payoffs=PayoffCalc(Thoughts, GameTheory); BestThought=NashEq(QualityScores, Payoffs); Execute(BestThought); Adapt(Outcome, Feedback)
If MCTS.done() then Execute(best_action, Context)
If MCTS.done() then Execute(best_action, Metrics)
Init(alpha, beta, gamma); Ethical_Logic=Deontological()->Virtue()->Utility(); Decision=argmax(Ethical_Logic); Feedback=Dynamic_Update(alpha, beta, Outcome)
Init(axioms, context); Eval=Dynamic_Score(axioms, context) >= len(axioms)/2
Init(Axioms, Thresholds, LearningRate, Feedback); Adapt=if ContextChange then Dynamic_Adjust_Threshold; if AssessmentComplete then Dynamic_Update_Criteria
Init(Ethical_Module(alpha, beta), Feedback); High_Level=Ethical_Module->Decision; Self_Assess=Calc_UTVs, Calc_CS; Adapt=Dynamic_Calc_DT, Dynamic_Calc_FLU
Base=MonteCarlo; Struct=3DWeb; NodeProps=[State, Reward, VisitCount, TaskType, TimeConstraints, ResourceAvailability]; Algos=[Dynamic_ContextAwareUCT, Dynamic_ContextSensitivePolicyNetwork, Dynamic_ContextSensitiveValueNetwork]; Adapt=[Dynamic_ContextualRL, Dynamic_ContextualTL]; Dynamic_ContextualQualityScore; Optimize=[Dynamic_ContextCaching, Dynamic_ContextConcurrency]
Init(alpha, beta, gamma); Ops=[Dynamic_Query, Dynamic_Update, Dynamic_Decide, Dynamic_Adapt]
Universal_Truth=Dynamic_Validate(axioms, context); Dynamic_InternalCheck; Optimize=[Dynamic_Data_Relevance, Dynamic_Threshold, Dynamic_Feedback_Loop]