Search Results for "cycle"

...Future AGI is especially relevant for agent-heavy products where reliability, regression testing, and safety checks matter before and after release. Its main value is turning AI agent development into a measurable engineering process instead of an informal cycle of prompting, guessing, and manual review.

...Unlike once-stateless chatbots, this platform captures context, memory, goals, preferences, and feedback to enable an AI that understands you and improves over time, using a full agentic stack rather than simple question-answer loops. PAI blends tools like browsing, code editing, execution, and more into a continuous Observe → Think → Plan → Execute → Verify → Learn cycle, letting the system refine its behavior with each use. Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator.

Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning,...

In this architecture, Javino enables processing the data coming from sensors as perceptions in ARGO's agent reasoning cycle. Then, one can restrict the list of perceptions delivered by Javino based on filters designed by the agent's programmer. The main contribution of ARGO is to enable the use of perception filters for programming robotic agents. Moreover, ARGO allows an agent to decide when to start or to stop perceiving from its sensors, to fix the interval between each perception and to control these filtering behavior in runtime. ...

...Ellipsis is built on top of the JMX architecture. Core Ellipsis components are implemented as MBeans which provides for basic ser-vices to execute agents and control their life-cycle. One of the core components is a special MBean (AgentModul) that makes it possible to load an agent powered by agent engines. Agent engines can execute one type of agent. Based on this mechanism, agents can be added dynamically to a running system, can be stopped (for example in case of an error) and restarted later. Ellipsis already comes with a predefined set of agents, offering basic services like agency, loader, holdingarea and migration.

nu-BDI is an extension of the Jason agent interpreter aimed at processing norms using constraints on the agent's instantiated plans. The core agent reasoning cycle now includes a component for processing norms and enforcing norm constraints.

RoboBeans is an interface to the "Robocup 2D Soccer Simulation Server" that allows developers to write Robocup teams\agents concentrating on behaviour and AI without having to worry about syntax of communication or network issues.

GAZES - Genetic Algorithm Zombie Eradication Simulation Grid-based simulator for zombie scenario. The units will use genetic algorithm to determine their actions on a cycle by cycle basis. This is a school project, so is likely on hiatus for summer.