Action Selection Module
Function: The Action Selection Module is the brain of the operation. It processes the perceived information against a set of predefined rules or a behavior table to decide the most appropriate action.
Components:
- Rule-Based System: A collection of if-then rules that map specific sensory inputs to actions.
- Behavior Table: A predefined table that lists possible actions based on different sensory inputs.
Role: This module processes the data from the Perception Module, matches it against predefined rules, and selects the best action to perform.
Reactive Agent in AI with Example
Agents are essential in the field of artificial intelligence (AI) because they solve complicated issues, automate processes, and mimic human behavior. A fundamental concept in this discipline is the idea of an agent. An agent is a software entity capable of sensing its environment, deciding what actions to take, and executing those decisions.
In this article, we will provide an extensive overview of reactive agents—quick-thinking and responding members of the AI community. We will explore their design and uses, discussing the fundamental terms, the elements that make up reactive agents, and how they perceive the world, make decisions, and carry out tasks. To ensure this tutorial is professional yet approachable for newcomers, we will also cover the benefits and drawbacks of reactive agents.
Table of Content
- Overview of Reactive Agents
- Architecture Components of Reactive Agents
- Perception Module
- Action Selection Module
- Execution Module
- Reactive Agent for Autonomous Obstacle Avoidance
- Implementation of Reactive Agent for Autonomous Obstacle Avoidance
- Applications of Reactive Agents
- Advantages of Reactive Agents
- Limitations of Reactive Agents
- Conclusion