<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Autonomy on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/autonomy/</link><description>Recent content in Autonomy on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/autonomy/index.xml" rel="self" type="application/rss+xml"/><item><title>Self-management</title><link>https://terms-en.ai-term-hub.com/en/terms/self_management/</link><pubDate>Sat, 18 Jul 2026 10:14:51 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/self_management/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>This concept encompasses the capacity of AI agents or systems to handle routine maintenance, resource allocation, and error correction independently. It includes features like auto-scaling, self-healing algorithms, and adaptive parameter tuning. By reducing reliance on manual oversight, self-management enhances system reliability, uptime, and efficiency in distributed cloud environments and edge computing setups.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Self-management in AI refers to autonomous systems&amp;rsquo; ability to monitor, optimize, and repair their own operations without human intervention.&lt;/p></description></item><item><title>Intelligent agent</title><link>https://terms-en.ai-term-hub.com/en/terms/intelligent_agent/</link><pubDate>Sat, 18 Jul 2026 10:02:57 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/intelligent_agent/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>An intelligent agent is a system capable of perceiving its surroundings through sensors or data inputs, processing this information using reasoning algorithms, and acting upon the environment via actuators or API calls to maximize goal achievement. Unlike static scripts, agents can adapt to dynamic conditions, learn from feedback, and operate autonomously over extended periods, making them essential for complex decision-making scenarios.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>An autonomous software entity that perceives its environment, reasons about actions, and executes tasks to achieve specific goals.&lt;/p></description></item><item><title>Belief–desire–intention model</title><link>https://terms-en.ai-term-hub.com/en/terms/beliefdesireintention_model/</link><pubDate>Sat, 18 Jul 2026 09:48:06 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/beliefdesireintention_model/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>The Belief-Desire-Intention (BDI) model is a cognitive architecture for designing autonomous agents that make rational decisions. Beliefs represent the agent&amp;rsquo;s knowledge about the world, desires are its goals, and intentions are the specific plans committed to achieving those goals. This model helps create agents that can dynamically adapt to changing environments by updating their beliefs, refining their desires, and revising their intentions. It is foundational in multi-agent systems and intelligent automation.&lt;/p></description></item><item><title>Autognostics</title><link>https://terms-en.ai-term-hub.com/en/terms/autognostics/</link><pubDate>Sat, 18 Jul 2026 09:47:03 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/autognostics/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Autognostics refers to the self-monitoring and self-repair mechanisms embedded within intelligent systems. It allows AI agents to detect anomalies, diagnose root causes of failures, and potentially correct themselves. This concept is vital for developing robust, autonomous systems that can operate reliably in dynamic environments. By continuously assessing their own health and accuracy, these systems reduce downtime and maintenance costs while enhancing overall operational resilience.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The capability of an AI system to self-diagnose its internal state, performance issues, or errors without human intervention.&lt;/p></description></item><item><title>Agentive logic</title><link>https://terms-en.ai-term-hub.com/en/terms/agentive_logic/</link><pubDate>Sat, 18 Jul 2026 09:45:08 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/agentive_logic/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>It extends traditional logic to account for agency, allowing systems to represent beliefs, desires, and intentions (BDI models). This logic enables agents to plan actions dynamically based on changing environments and internal states. By formalizing how agents perceive their world and choose actions to achieve goals, agentive logic supports the development of sophisticated autonomous systems capable of complex, goal-directed behavior in uncertain environments.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Agentive logic refers to the formal reasoning frameworks used to model the intentions, goals, and decision-making processes of autonomous agents.&lt;/p></description></item><item><title>Planning</title><link>https://terms-en.ai-term-hub.com/en/terms/planning/</link><pubDate>Sat, 18 Jul 2026 09:41:54 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/planning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Planning in AI involves determining a sequence of actions that will lead from an initial state to a desired goal state. It requires reasoning about the effects of actions and the constraints of the environment. Classical planning uses symbolic representations, while modern approaches may integrate reinforcement learning or large language models to handle complex, dynamic, or partially observable environments, enabling autonomous agents to make strategic decisions.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The cognitive process of generating a sequence of actions to achieve specific goals within a defined environment.&lt;/p></description></item><item><title>Agentic</title><link>https://terms-en.ai-term-hub.com/en/terms/agentic/</link><pubDate>Sat, 18 Jul 2026 09:39:58 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/agentic/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>The term &amp;lsquo;agentic&amp;rsquo; describes AI agents that operate with a high degree of autonomy. Unlike passive models that simply predict text or classify data, agentic systems can break down complex objectives into sub-tasks, use tools, interact with environments, and iterate on their actions to solve problems. This paradigm shifts AI from being a reactive tool to a proactive collaborator. These systems often employ memory, planning mechanisms, and reflection loops to improve performance over time, enabling them to handle dynamic and unstructured real-world scenarios effectively.&lt;/p></description></item><item><title>Autonomous</title><link>https://terms-en.ai-term-hub.com/en/terms/autonomous/</link><pubDate>Sat, 18 Jul 2026 09:30:18 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/autonomous/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Autonomy in AI refers to the ability of a system to perceive its environment, make decisions, and execute actions without direct human control. Unlike simple automation, autonomous systems adapt to changing conditions and handle uncertainty. This is critical in fields like self-driving cars, drones, and smart home devices, where real-time decision-making and environmental interaction are essential for safe and effective operation.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Describes systems capable of making decisions and acting independently in dynamic environments.&lt;/p></description></item><item><title>Agents</title><link>https://terms-en.ai-term-hub.com/en/terms/agents/</link><pubDate>Sat, 18 Jul 2026 09:30:04 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/agents/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>AI agents are software programs or systems capable of perceiving their surroundings through sensors (inputs), processing information, and executing actions via actuators (outputs) to achieve defined objectives. They operate autonomously within an environment, often employing reasoning, planning, and learning capabilities to navigate complex tasks, make decisions, and interact with other agents or humans effectively.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Autonomous entities that perceive their environment and take actions to achieve specific goals.&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>Perception&lt;/li>
&lt;li>Autonomy&lt;/li>
&lt;li>Goal-Oriented&lt;/li>
&lt;li>Actuation&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>Chatbots&lt;/li>
&lt;li>Autonomous Vehicles&lt;/li>
&lt;li>Trading Bots&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/environment/">Environment&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/policy/">Policy&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/multi-agent-systems/">Multi-Agent Systems&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/rationality/">Rationality&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>