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