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What are AI Agents? Definition and Types - Tpoint Tech
- 1. AGENT AI www.tpointtech.com Exploring Cutting-Edge TechnologyShaping The Future
- 2. Artificial Intelligence isdefined as the study of rational agents. A rational agent may take the form of a person, firm, machine, or software to make decisions. It works with the best results after considering past and present perceptions An AI system is made up of an agent and its environment. Agents work in their environment, and the environment may include other agents. www.tpointtech.com AI ABOUT
- 3. EXAMPLES: AGENT A softwareagent has keystrokes, file contents, received network packages that act as sensors and are displayed on the screen, files, sent network packets to act as actuators The human agent has eyes, ears, and other organs that act as sensors, and hands, feet, mouth, and other body parts act as actuators. A robotic agent consists of cameras and infrared range finders that act as sensors and various motors that act as actuators. www.tpointtech.com
- 5. OF AGENTS TYPES Simple reflexagent Model-based reflex agent Target-based agent Utility-based agent Learning agent www.tpointtech.com
- 6. AGENT SIMPLE REFLEX Simple reflexagents act only on the current percept, ignoring percept history. They use condition-action rules: if a condition is true, they perform the corresponding action. These agents work well only in fully observable environments. In partially observable settings, they risk falling into infinite loops—unless they randomize actions to avoid them. Minimal intelligence. There is no knowledge of the non-perceptual parts of the state. It is usually too large to generate and store. If a change occurs in the environment, the rules collection needs to be updated. The problems with simple reflex agents are:
- 8. AGENTS: MODEL-BASED REFLEX A model-based agentuses a world model to handle partially observable environments. It tracks an internal state based on percept history, updating it with each new percept to represent unseen parts of the world. Updating the state requires information about: How the world develops independently of the agent, and How the agent's actions affect the world.
- 10. www.tpointtech.com Goal-based agents makedecisions by evaluating how close they are to their goals. Each action aims to reduce the distance to the target. Their decision-making is flexible, supported by modifiable knowledge, and involves discovery and planning. Their behavior is easy to change. AGENT GOAL-BASED
- 12. AGENT UTILITY-BASED Utility-based agents usea utility function to choose the best action among alternatives by maximizing expected happiness or satisfaction. They consider not just goal achievement but also factors like speed, safety, or cost. Utility maps each state to a value showing how desirable it is. www.tpointtech.com
- 14. AGENT LEARNING www.reallygreatsite.com A learning agentin AI is the type of agent that can learn from its past experiences or it has learning capabilities. It starts to act with basic knowledge and then is able to act and adapt automatically through learning.
- 15. 1. Learning element:It is responsible for making improvements by learning from the environment A learning agent has mainly four conceptual components, which are: 2. Critic: The learning element takes feedback from critics, which describe how well the agent is doing to a fixed performance standard. 3. Performance element: It is responsible for selecting external action 4. Problem Generator: This component is responsible for suggesting actions that will lead to new and informative experiences.
- 17. www.reallygreatsite.com ENVIRONMENT THE NATURE OF Someprograms run in simple artificial environments, limited to keyboard input, file systems, and screen output. In contrast, software agents, also known as softbots, operate in rich, complex simulated environments, handling a wide range of real-time tasks. For example, a softbot that recommends items based on a customer's online behavior operates in both real and artificial environments. A well-known artificial environment is the Turing Test, where human and software agents are tested equally. This is highly challenging, as it's hard for a software agent to match human performance.
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