Reactive Agent

Last updated: May 21, 2019

What Does Reactive Agent Mean?

Reactive agents are software agents that carry out a simple task of retrieving pre-set behaviors similar to reflexes.

Reactive agents do not maintain the internal state, unlike deliberative agents. Finding a difference between reactive agents and deliberative agents can be indistinct though. It can simply be said that an agent that has no internal state is a reactive agent.

Safeopedia Explains Reactive Agent

In contrast to a reactive agent, a deliberative agent maintains its internal state and predicts the effects of actions. Deliberative agents act more like thinking agents and search through a space of behaviors while maintaining their internal state. Reactive agents consume fewer system resources, which is why they cannot generate results that are as good as those from deliberative agents. Unlike reactive agents, deliberative agents cannot re-plan the actions quickly. It is entirely possible to use a few reactive agents in place of a deliberative agent in many cases.

Reactive agents have their limitations, though. They cannot just react automatically on the basis of information from the external environment. Reactive agents must have enough data available in the local or internal environment to figure out a satisfactory action. In the decision-making process, it is difficult for reactive agents to take into account the external or non-local information. Reactive agents do not understand the relationship between environmental and individual behavior. These agents cannot understand the overall behavior either. To create such reactive agents that can understand these behaviors is a challenging task. Moreover, the methodology to create such agents does not exist. Reactive agents sense and act through various means.

More-complex reactive agents can remember properties and can also store internal models. The actions are taken based on the stored internal models and the properties that are remembered by these reactive agents. If we take into account the autonomous robots, the formation maintenance is studied through reactive agents that are non-communicating and homogenous.

The primary goal of autonomous robots is to move in a particular formation. But when these robots move, they face obstacles at some point along the way. This results in the distraction of one or more robots from the straight line. Once the robots pass through the obstacle, they must regain the straight path that they were following before. The reactive agents come into action to complete this task.

The data from the sensors is converted into the motion vectors, and this helps to avoid the obstacles. Reactive agents also take into account the position of other robots, and they follow the same pattern to reach the original destination. Thus, the movement of these robots in this fashion is the collective response of these motion vectors.


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