About This Chapter
Intelligent Agents - Chapter Summary
Our instructors offer the comprehensive overview you need to deepen your understanding of intelligent agents. Engaging lessons closely examine definitions and examples of simple reflex agents, learning agents and more. Feel free to review these lessons any time your schedule permits via your computer or mobile device of choice. Questions that arise during your studies can be submitted to our experts via the Dashboard. When you're ready, take the multiple-choice quizzes and broader exam to gauge your grasp of the entire chapter. When you're finished, you will be ready to do the following:
- Define and list types of intelligent agents
- Discuss the uses and share examples of simple reflex agents
- Explain how model-based agents interact with the environment
- Share the definition and examples of goal-based agents
- Describe how utility-based agents make decisions based on utility
- List and discuss components of learning agents
1. Intelligent Agents: Definition, Types & Examples
An intelligent agent is a component of artificial intelligence that perceives its environment and reacts accordingly. In this lesson, you'll learn more about intelligent agents, their five types, and see several examples.
2. Simple Reflex Agents: Definition, Uses & Examples
A simple reflex agent responds to current conditions with pre-determined actions, thanks to the condition-action rule. In this lesson, you'll learn more about these agents and how they operate.
3. Model-based Agents: Definition, Interactions & Examples
A model-based reflex agent relies on its perceptual history and internal model of the external world to act on (and interact with) its environment. In this lesson, you'll learn more about this intelligent agent and some examples of how it works.
4. Goal-based Agents: Definition & Examples
A goal-based agent has flexibility to adjust its actions based on successfully reaching a goal. In this lesson, you'll learn more about this agent in artificial intelligence and how it differs from a model-based agent.
5. Utility-based Agents: Definition, Interactions & Decision Making
A utility-based agent makes decisions based on the maximum utility of its choices. In this lesson, you'll learn more about these intelligent agents and how they interact with their environments.
6. Learning Agents: Definition, Components & Examples
A learning agent is able to act and adapt based on new information. In this lesson, you'll learn more about learning agents and the four components necessary for their knowledge to expand.
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Other chapters within the Computer Science 311: Artificial Intelligence course
- Fundamentals of Artificial Intelligence
- Using Artificial Intelligence in Searches
- Constraint Satisfaction in Artificial Intelligence
- Logical Agents & First-Order Logic
- Learning & Reasoning in Artificial Intelligence
- The Present & Future of Artificial Intelligence
- Required Assignment for Computer Science 311