This content originally appeared on DEV Community and was authored by Preshen Govender
Hey everyone,
I’ve been working on documenting the cognitive architectures behind the KN-LAB frameworks, and I’d love to share the ideas here to get feedback from people thinking about AI, AGI, and cognitive systems.
🔹 Why Cognitive Architectures?
Cognitive architectures are essentially the blueprints for intelligence: how knowledge is represented, processed, and used in reasoning, learning, and decision-making. They sit at the intersection of neuroscience-inspired models, symbolic AI, and adaptive learning systems.
🔹 The Foundations
In building KN-LAB, I looked at multiple cognitive traditions:
Information Processing Models → sensory → perception → memory → executive control
Connectionist Models → parallel, distributed, adaptive networks (graceful degradation, content-addressable memory)
Symbolic Models → rule-based reasoning, explicit symbols, hierarchical knowledge
Hybrid Models → blending subsymbolic learning with symbolic reasoning
🔹 Inspirations from Existing Architectures
KN-LAB borrows from and integrates lessons from major architectures like:
ACT-R (declarative + procedural knowledge, goal modules)
SOAR (problem-solving + chunking + reinforcement learning)
CLARION (explicit vs. implicit knowledge systems)
LIDA (global workspace + attentional control)
🔹 The KN-LAB Approach
At its core, KN-LAB emphasizes:
Knowledge Integration → semantic nets, ontologies, embeddings, knowledge graphs
Adaptive Processing → reasoning + learning tuned by context
Contextual Awareness → environment and task-sensitive cognition
Continuous Learning → evolving knowledge representations
The architecture is organized into layers:
Knowledge Foundation Layer (graphs, ontologies, distributed memory)
Cognitive Processing Layer (perception, reasoning, learning, attention, executive control)
Adaptive Integration Layer (context management, goals, conflict resolution, meta-cognition)
Interface Layer (I/O, explanation, collaboration with humans/agents)
🔹 Processing Dynamics
Information flows through: input → pattern recognition → context integration → knowledge activation → reasoning → decision → output.
Control happens via top-down goals, bottom-up stimuli, attentional focus, and metacognition.
🔹 Theoretical Implications
Bounded rationality & heuristic reasoning (good-enough > optimal)
Emergence of creativity, insight, analogical thinking, conceptual blending, adaptive expertise
🚀 Why This Matters
KN-LAB isn’t just another knowledge system — it’s an attempt to design computational cognitive architectures that are context-aware, adaptive, and emergent.
I see it as a potential backbone for:
More generalizable AI systems
Collaborative human–AI interfaces
Research toward AGI grounded in cognitive science
My Question to You:
👉 How do you see hybrid symbolic–connectionist–contextual architectures fitting into the future of AI/AGI?
👉 Do you think emergent properties like creativity and analogical reasoning can be engineered, or will they always remain side-effects of scale?
Would love to hear your thoughts, critiques, and additions.
This content originally appeared on DEV Community and was authored by Preshen Govender
Preshen Govender | Sciencx (2025-09-23T22:22:17+00:00) [Discussion] Cognitive Architectures in KN-LAB Frameworks — Toward Adaptive, Context-Aware Intelligence. Retrieved from https://www.scien.cx/2025/09/23/discussion-cognitive-architectures-in-kn-lab-frameworks-toward-adaptive-context-aware-intelligence-2/
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