jason-RL adds a reinforcement learning layer to the Jason multi-agent framework, allowing AgentSpeak BDI agents to learn action-selection policies via reward feedback. It implements Q-learning and SARSA algorithms, supports configuration of learning parameters (learning rate, discount factor, exploration strategy), and logs training metrics. By defining reward functions in agent plans and running simulations, developers can observe agents improve decision making over time, adapting to changing environments without manual policy coding.

NobleAI leverages advanced algorithms to automate complex decision-making tasks, allowing users to efficiently analyze data, generate reports, and optimize workflows. The AI facilitates seamless collaboration within teams by providing real-time insights and recommendations tailored to specific business needs. With its easy-to-use interface, NobleAI empowers users to harness the full potential of their data, ensuring informed decisions are made swiftly and accurately.