What Are Model Based Reflex AI Agents?
Current concepts are the rule of the day for model based reflex AI agents. This type of AI agent focuses on an internal state focusing on the unobservable word. So what exactly does that mean? In short, this AI agent is capable of updating it's internal state that is based off to specific factors or entities. Model based rflex agents are a type of artificial intelligence that focuses on how the world evolves in a way that is independent or seperate from the AI agent itself. Also, the model based reflex agent uses this data to determine how the agent's action would affect the world.
There are some varaibles associated with a model based reflex agent as this type of artificial intelligence AI agent can be programmed in a cautionary model base that would look at specific consequences of the actions of the agent before executing them. These agents follow the condition action rule where appropriate actions are specific to any given situation. As discussed with the simple action AI agent, model reflex agents are a type of agent that is more complex. These agents employ their internal state in a way to assess certain conditions during action and decision processes of the agent.
A model based reflex agent works in four specific stages to execute actions. These actions work together to perform conditons upon it's internal state. These stages are:
1) Sense: Using the agent's sensors, the AI agent uses this stage to conceive the current state of the world
2) Model: The AI agent then uses this sense to construct an internal model of the world determined by what it sees.
3) Reason: Once the model of the world is determined, the model based reflex AI agent will decide on how to act based on a set of predfined rules that are usually programmed into the agent from human influence
4) Act: This last stage is simply carrying out the act the model based agent has chosen after going through the previous stages.
There are many advantages of model based reflex AI agents as this type of AI agent is quick and efficient in it's decision making process. This is all based on it's understanding of the world. These AI agents are better equipped to make more accurate decisions based on it's internal model. These AI agents are able to adapt as they can update information from their internal model and are more informed to determine conditions. There are some disadvantages to model based reflex AI agents as this type of agent can be quite expensive to build and also manitain their models. The real world environment may not always be accurate as real world scenarios can be quite complex. If potential situations arise, this AI agent may not always anticipate certain situations. Unlike simple reflex agents, model based agents need to be updated regularly as new information is required to perform thesir processes. It's important for accurate and updated information supplied to the agent, which takes time and money to complete.