Hierarchical policy learning is a method in machine learning where complex tasks are broken down into simpler sub-tasks. Each sub-task is handled by its own policy, and a higher-level policy decides which sub-policy to use at each moment. This approach helps systems learn and perform complicated behaviours more efficiently by organising actions in layers, making…
Category: Autonomous Systems
Autonomous Prompt Selection
Autonomous prompt selection is when an artificial intelligence system chooses the most appropriate prompt or instruction by itself, without needing human direction. This allows the AI to decide how best to approach a task based on the situation or input it receives. The aim is to make AI systems more adaptable and capable of handling…
Agent Coordination Logic
Agent Coordination Logic refers to the rules and methods that allow multiple software agents to work together towards shared goals. These agents can be computer programs or robots that need to communicate and organise their actions. The logic ensures that each agent knows what to do, when to do it, and how to avoid conflicts…
Agent Mood Modulation
Agent mood modulation refers to the ability of artificial agents, such as robots or virtual assistants, to adjust their displayed emotional state or mood. This can help make interactions with humans feel more natural and engaging. By altering their responses based on mood, agents can better match the emotional tone of a conversation or environment,…
Dynamic Prompt Autonomy
Dynamic Prompt Autonomy refers to the ability of an AI or software system to modify, generate, or adapt its own instructions or prompts without constant human input. This means the system can respond to changing situations or user needs by updating how it asks questions or gives tasks. The goal is to make interactions more…
Multi-Agent Consensus Models
Multi-Agent Consensus Models are mathematical frameworks that help groups of independent agents, such as robots, computers, or sensors, agree on a shared value or decision. These models describe how agents update their information by communicating with each other, often following simple rules, until everyone reaches a common agreement. Consensus models are important for coordinating actions…
Multi-Agent Evaluation Scenarios
Multi-Agent Evaluation Scenarios are structured situations or tasks designed to test and measure how multiple autonomous agents interact, solve problems, or achieve goals together. These scenarios help researchers and developers understand the strengths and weaknesses of artificial intelligence systems when they work as a team or compete against each other. By observing agents in controlled…
Agentic Workload Delegation
Agentic workload delegation is the process of assigning tasks or responsibilities to software agents or artificial intelligence systems, allowing them to handle work that would otherwise be done by humans. This approach helps distribute tasks efficiently, especially when dealing with repetitive, complex, or time-consuming activities. It relies on agents that can make decisions, manage their…
Self-Healing Prompt Systems
Self-Healing Prompt Systems are automated setups in which AI prompts can detect when they are not producing the desired results and make adjustments to improve their performance. These systems monitor their own outputs, identify errors or shortcomings, and revise their instructions or structure to try again. This approach helps maintain consistent and reliable AI responses…
Delivery Routing Engine
A delivery routing engine is a software system that calculates the most efficient routes for delivering goods or services to multiple locations. It uses data such as addresses, traffic conditions, delivery windows, and vehicle capacities to plan routes that minimise travel time and costs. Companies use delivery routing engines to improve their logistics operations, reduce…