Overview of Reactive Agents
When changes occur in its surroundings, a reactive AI agent reacts immediately to them without the need of internal models or convoluted decision-making procedures. These agents respond to their surroundings by eliciting basic rules or behaviors. Reactive agents are sentient entities, that respond to their environment instinctively, much like insects do to different stimuli.
Consider an example of basic thermostat. It continuously senses the ambient temperature (perception) and depending on the reading (sensory input) , it activates the air conditioning or heating system (activity). The thermostat just responds to the present environmental conditions; it doesn’t take historical temperature readings into account or forecast future requirements.
To help us comprehend better, here’s a summary of some important terminology:
- Agent: An agent is a software entity with the ability to sense its surroundings decide what to do, and act.
- Environment: The physical location where, the agent functions. It might be virtual (like a gaming world) or tangible (like a robot’s workstation).
- Perception: Perception is the process of using sensors to collect data about the surroundings (e.g., temperature sensor in a thermostat).
- Action: What the agent does to change its surroundings or accomplish, its objectives.
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