MIDCA (Meta-cognitive Integrated Dual-Cycle Architecture) is an open-source AI framework allowing development of intelligent agents that perceive environments, generate and prioritize goals, plan actions, execute tasks, and reflect on performance for learning. It integrates dual-cycles for reactive and deliberative behaviors, supporting metacognitive modules for self-monitoring, evaluation, and memory. MIDCA facilitates research in autonomous decision-making, adaptive behaviors, and metacognitive processes in agents across diverse domains.
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#cognitive architecture
#intelligent agents
#AI decision-making
#reactive and deliberative processing
#goal prioritization
#modular design
#metacognition in AI
#data processing
#planning and execution
#Open-source AI
#autonomous learning
#dynamic adaptability
#memory management
#Cross-platform AI
#Rapid Prototyping
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#agent design
#intelligent behavior
#self-reflection AI
#sensor data interpretation
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MIDCA
MIDCA (Meta-cognitive Integrated Dual-Cycle Architecture) is an open-source AI framework allowing development of intelligent agents that perceive environments, generate and prioritize goals, plan actions, execute tasks, and reflect on performance for learning. It integrates dual-cycles for reactive and deliberative behaviors, supporting metacognitive modules for self-monitoring, evaluation, and memory. MIDCA facilitates research in autonomous decision-making, adaptive behaviors, and metacognitive processes in agents across diverse domains.
AI Platforms & Frameworks
Predictive Analytics
Mobile Applications
Web Tools
#cognitive architecture
#intelligent agents
#AI decision-making
#reactive and deliberative processing
#goal prioritization
#modular design
#metacognition in AI
#data processing
#planning and execution
#Open-source AI
#autonomous learning
#dynamic adaptability
#memory management
#Cross-platform AI
#Rapid Prototyping
#AI Research Tools
#agent design
#intelligent behavior
#self-reflection AI
#sensor data interpretation
...
Featured
What is MIDCA?
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.
Who will use MIDCA?
- AI researchers in cognitive architectures
- Robotics developers
- Academic institutions and students
- Defense and aerospace R&D teams
- Cognitive scientists
How to use the MIDCA?
- Step1: Install Java, Python, and required dependencies.
- Step2: Clone the MIDCA GitHub repository from http://www.midca-arch.org/ or GitHub mirror.
- Step3: Configure the environment by setting classpaths and scenario files.
- Step4: Define domain models, tasks, and perception modules.
- Step5: Implement or customize planning, execution, and metacognitive components.
- Step6: Run simulations using provided scripts or integrate with robotic platforms.
- Step7: Monitor logs, analyze performance, and iterate on agent design.
Platform
- mac
- windows
- linux
MIDCA's Core Features & Benefits
The 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
The Benefits
- Modular and extensible open-source architecture
- Supports metacognitive research and development
- Dual-cycle design for adaptability
- Cross-platform compatibility
- Ready-to-run scenarios for rapid prototyping
MIDCA's Main Use Cases & Applications
- Autonomous robotics decision-making experiments
- Cognitive modeling in academic research
- Adaptive agent behavior prototyping
- Metacognitive process analysis
- Teaching cognitive architecture concepts
MIDCA's Pros & Cons
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
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
FAQs of MIDCA
MIDCA Company Information
- Boston University MIDCA Project
MIDCA Reviews
5/5
MIDCA's Main Competitors and alternatives?
- Soar Cognitive Architecture
- ACT-R
- ROSPlan
- Jason BDI Framework
- CLIPS
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