MIDCA is a modular cognitive architecture designed to support the full cognitive loop of intelligent agents. It processes sensory inputs through a perception module, interprets data to generate and prioritize goals, leverages a planner to create action sequences, executes tasks, and then evaluates outcomes through a metacognitive layer. The dual-cycle design separates fast reactive responses from slower deliberative reasoning, enabling agents to adapt dynamically. MIDCA’s extensible framework and open-source codebase make it ideal for researchers and developers exploring autonomous decision-making, learning, and self-reflection in AI agents.
MIDCA Core Features
Dual-cycle cognitive processing (reactive and deliberative)
Perception and interpretation modules
Goal generation and prioritization
Integrated planning and execution pipeline
Metacognitive monitoring and evaluation
Learning and memory management
MIDCA Pro & Cons
The Cons
Supports only Python 2.7, an outdated version of Python
May have a steep learning curve for beginners
Limited recent updates or community activity visible
The Pros
Open source with active GitHub repository
Provides a unique metacognitive architecture for AI
Includes demos and extensive documentation
Enables monitoring and control of cognitive cycles
