Agent Types In Artificial Intelligence – Agent and environment are two pillars in Artificial Intelligence, we aim to build intelligent agents and work in the environment. If you broadly consider that the agent is the solution and the environment is the problem.
In simple words, even a beginner or a researcher can understand it and the agent is defined as the playing field and the environment as the field.
Agent Types In Artificial Intelligence
Defining both the agent and the environment with several examples to capture the reader’s attention and context. Agents and environments are not that simple. There are types that exist in both cases, the diagram below summarizes these.
Artificial Intelligence: Agents And Environment
Better to refer to the first and second chapters in A Modern Approach by Stuart Russell, Peter Norvig. Now let us define the types of agents and environments in an easy to understand way for a novice or starter in AI. While defining the above we will come across other concepts which we will come across in different applications or domains.cc
Environment is the place where the agent is going to act. In general, the environment provides agents with possible rewards, conditions, actions.
The work environment in AI is clearly vast. We identify some dimensions along which work environments can be classified. Understanding AI requires understanding the environment.
If an agent’s sensors give it access to the full state of the environment at each point in time, we say that the task environment is fully observable.
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An environment is partially observable due to noise and inaccurate sensors, or because parts of the state are missing from the sensor data.
Only one agent participates in the environment is a single agent. More than one agent interact with the environment is multi agent.
If one agent (entity) is maximizing its performance over the other agent (entity) in the environment, then it is a competitive multi-agent environment. An agent can be treated based on physical laws (behavior on physical laws).
If the next state of the environment is completely determined by the current state and the action performed by the agent, then we say that the environment is deterministic, otherwise it is stochastic.
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In a multi-agent environment, uncertainty arises purely from the actions of other agents. In deterministic, the actions of other agents are unable to be predicted by any other agent (each agent).
An environment is uncertain if it is not fully observable or is not deterministic. A non-deterministic environment is one in which actions are characterized by their possible consequences, but have no probabilities associated with them.
Where stochastic usually means that uncertainty about outcomes is quantified in terms of probabilities. Non-deterministic environmental descriptions are usually associated with the measured performance that requires the agent to be successful for all possible outcomes of its actions.
In episodic environments, the agent’s experience is divided into atomic episodes. In each episode the agent receives a concept and then performs a single action. The next episode does not depend on the actions taken in the previous episode. Many classification tasks are episodic.
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In a sequential environment, the current decision may affect all future decisions. Episodic environments are much simpler than sequential ones because the agent is not required to think ahead.
Static environments are easier to handle because the agent does not need to keep observing the world. Neither should worry about the passage of time while deciding on an action.
If the environment can change during the course of an agent’s deliberations, we say that the environment is dynamic for that agent, otherwise the environment is static.
In dynamic environments constantly asking the agent what it wants to do, if it hasn’t decided yet, is counted as deciding to do nothing.
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Quasi-dynamic environment: If the environment itself does not change with the passage of time but the agent’s performance scores, then the environment is quasi-dynamic.
The state of the environment in these 2 different environments, the way time is handled, and applies to the agent’s perceptions and actions. ChessBase – Discrete, Continuous – Driving Autonomous Vehicles.
Both of these specifically refer to the environment and not the agent; If there is a known environment, the results of all actions are given. If the environment is unknown, the agent needs to learn how it works in order to make good decisions. These environments are good examples of exploitation (known environment) and exploration (unknown environment) that fall under Reinforcement Learning.
Agent is the solution to our problem. The agent needs the intelligence that the AI provides to operate in the environment. Each agent has to have its own agent program, agent function, mapping from perception into action. The diagram below describes it.
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Agent program: It runs on some kind of computing device with physical sensors and actuators – this is called architecture.
Therefore agent = architecture + program; Obviously, the program must be appropriate for the architecture. Example for program action and architecture is shown below.
The architecture makes assumptions from the available sensors for the program, runs the program and feeds the program’s action choices to the actuators as they are generated. Let us discuss the concepts on the agent side.
The agent program structure takes as input the current perception from the sensor and returns an action to the actuators.
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Agent function that takes as input the entire concept history. Distinguish between an agent program, which takes a current concept as input, and an agent function, which takes an entire concept history.
There are four basic types of agent programs that comprise the underlying principles of nearly all intelligent systems. They are Simple Reflex Agent, Model-Based Reflex Agent, Goal-Based Agent and Utility-Based Agent. Each agent program links specific components in a specific way to generate actions.
It is the simplest agent, whereas these agents choose actions directly from perception and ignore the perception history. Simple reflex behaviors also occur in more complex environments. Let us define the condition-action rule.
Condition Action Rule: This is the connection and is defined as “Processing on visual input to establish a condition”, then it triggers an action in the agent program. This connection is called the “condition-action rule”. Schematic diagram of a simple reflex agent defined as follows
Solution: Types Of Artificial Intelligence Agents
When the agent partially observes the environment, the agent needs to keep track of the part of the world it cannot yet observe. That is, the agent must maintain some kind of internal state, which depends on the history of perception and on unseen aspects of the current state. i.e. graphically it is defined as
Model: Knowledge of “how the world works”, whether implemented in simple Boolean circuits or in full scientific theories, is called a model of the world. Agents that use such models are called model-based-agents.
Search and planning are subfields of AI that achieve the agent’s goal. The behavior of a goal-based agent can be easily changed to go to a different destination by specifying the destination as the destination. Goal-based agent composition is defined as
Goals provide a distinction between happy and unhappy states, specified with “happy” and “unhappy states”. Being happy and unhappy doesn’t mean anything scientifically, economists and computer scientists use the term “utility”.
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Goal-based agents have several advantages over utility-based agents in terms of flexibility and learning. Utility Agents Make Rational Decisions When Goals Are Inadequate 1) The utility function specifies the appropriate trade-off. 2) The usefulness the likelihood of success provides can be weighed against the importance of the goals.
A rational utility-based agent chooses the action that maximizes the expected utility of the action’s consequences. The structure of the utility-based agent structure is described as follows.
Examples of agents and environments are numerous because of their contexts, applications, and needs. Possible and well-known agents and environments listed in the diagram below.
Before developing any type of intelligent agent for our applications it is imperative to understand the agents and the environment. In order to design any intelligent agent based on the environment, it is necessary to understand what kind of agent needs to be created, what is needed for it, what kind of equipment etc. It is better to refer to the first and second chapters in A Modern Approach by Stuart Russell, Peter Norvig..
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This article is based on the second chapter in Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig. Understanding the AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that argument.
Each artificial intelligence (AI) problem is a new universe of complexities and unique challenges. Too often, the most challenging aspect of solving an AI problem isn’t about finding the solution but understanding the problem itself. Paradoxical as it sounds, even the most experienced AI experts are guilty of proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we base our reasoning on two main aspects: datasets and models. However, this argument is ignoring what may be considered the most challenging aspect of the AI problem: the environment.
When designing Artificial Intelligence (AI) solutions, we spend a lot of time focusing on aspects such as the structure of the learning algorithms [ex: supervised, unsupervised, semi-supervised], the architecture of a neural network [ex: convolutional , recurrent… ] or characteristics of the data [ex: labeled, unlabeled…]. However, often little attention is paid to the nature of the environment in which the AI solution operates. As it turned out, the characteristics of
