Cognitive Partner Agent Architecture

A Cognitive Partner Agent Architecture is a software architecture model that structures cognitive partner agent components through layered design patterns (enabling human-AI collaboration via cognitive processing layers).

• AKA: Cognitive Agent Design Pattern, AI Partnership Architecture, Collaborative Agent Blueprint, Human-AI System Architecture.
• Context:
  • It can typically organize Memory Layers including working memory, episodic memory, and semantic memory modules.
  • It can typically structure Reasoning Engines through symbolic reasoning, probabilistic inference, and causal models.
  • It can typically implement Learning Pipelines via supervised pathways, reinforcement loops, and transfer mechanisms.
  • It can typically design Planning Modules with goal hierarchy, action selection, and resource scheduling.
  • It can typically configure Perception Interfaces for multimodal input, feature encoding, and context extraction.
  • It can typically establish Communication Layers through language processing, dialog control, and explanation generation.
  • It can typically incorporate Partnership Protocols including turn management, initiative sharing, and collaboration patterns.
  • It can often enable Meta-Reasoning Layers for self-reflection, strategy adaptation, and performance monitoring.
  • It can often support Social Cognition Modules through theory of mind, emotion recognition, and social norms.
  • It can often facilitate Tool Interface Layers for API integration, service invocation, and resource access.
  • It can often implement Safety Governors through constraint checking, ethical filters, and human overrides.
  • It can range from being a Shallow Cognitive Partner Agent Architecture to being a Deep Cognitive Partner Agent Architecture, depending on its processing depth.
  • It can range from being a Monolithic Cognitive Partner Agent Architecture to being a Modular Cognitive Partner Agent Architecture, depending on its component separation.
  • It can range from being a Synchronous Cognitive Partner Agent Architecture to being an Asynchronous Cognitive Partner Agent Architecture, depending on its processing model.
  • It can range from being a Rule-Based Cognitive Partner Agent Architecture to being a Learning-Based Cognitive Partner Agent Architecture, depending on its adaptation mechanism.
  • ...
• Example(s):
  • Classical Cognitive Partner Agent Architectures, such as:
    • ACT-R Architecture implementing human cognitive models.
    • SOAR Architecture providing unified cognition theory.
    • CLARION Architecture combining implicit-explicit processing.
  • Modern Cognitive Partner Agent Architectures, such as:
    • Transformer-Based Architecture using attention mechanisms (demonstrating deep processing).
    • ReAct Architecture combining reasoning with action (showing meta-reasoning layers).
    • Chain-of-Thought Architecture enabling step-by-step reasoning (exhibiting explanation generation).
  • Hybrid Cognitive Partner Agent Architectures, such as:
    • Neuro-Symbolic Architecture merging neural learning with logical reasoning (implementing safety governors).
    • BDI-ML Architecture combining belief-desire-intention with deep learning (supporting social cognition).
    • Cognitive-LLM Architecture integrating cognitive models with language models.
  • Enterprise Cognitive Partner Agent Architectures, such as:
    • Microsoft Semantic Kernel organizing plugin ecosystems (facilitating tool interfaces).
    • IBM Watson Architecture structuring service pipelines.
    • Google Vertex AI Architecture managing model orchestration.
  • Specialized Cognitive Partner Agent Architectures, such as:
    • Medical Diagnosis Architecture optimizing clinical reasoning.
    • Financial Trading Architecture balancing risk-reward calculations.
    • Educational Tutoring Architecture adapting learning pathways.
  • ...
• Counter-Example(s):
  • Simple Neural Network, which lacks architectural layers and cognitive components.
  • Microservice Architecture, which lacks cognitive processing and partnership protocols.
  • Event-Driven Architecture, which lacks goal representation and planning modules.
  • REST API Architecture, which lacks reasoning engines and learning pipelines.
  • Database Schema, which lacks agent behavior and adaptation mechanisms.
• See: Cognitive Partner Agent, Cognitive Partner Agent System, Software Architecture Pattern, Cognitive Architecture, Agent-Oriented Architecture, Layered Architecture, BDI Architecture, Blackboard Architecture, Multi-Agent Architecture, Human-AI Interface Design, Cognitive Computing Architecture, AI System Design Pattern, Hybrid Architecture Model.

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