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.

The Java Action Linearprogram module provides a specialized action for the LightJason framework that allows agents to model and solve linear optimization tasks. Users can configure objective coefficients, add equality and inequality constraints, select solution methods, and run the solver within an agent’s reasoning cycle. Once executed, the action returns the optimal variable values and objective score which agents can use for subsequent planning or execution. This plug-and-play component abstracts solver complexity while maintaining full control over problem definitions through Java interfaces.