Agent Program In Artificial Intelligence – Agent and environment are the two pillars of artificial intelligence, our goal is to build intelligent agents and work in the environment. If you think broadly, the agent is the solution and the environment is the problem.
Simply put, even a novice or explorer can understand it, and the agent is defined as the game and the environment as the land.
Agent Program In Artificial Intelligence
Defining the agent and the environment with several examples so that the reader can draw attention and its context. Agents and environments are not that simple. There are types that exist in both cases, summarized in the diagram below.
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It is better to refer to more chapters 1 and 2 in A Modern Approach by Stuart Russell, Peter Norvig. We now define agent and environment types so that they are easy to understand for a novice or AI novice. By defining the above we will come across other concepts that we will come across in different applications or domains.cc
The environment is where the agent is going to work. In general, the Environment assigns potential rewards, states, actions to agents.
AI task environments are obviously vast. We identify a small number of dimensions along which task environments can be classified. Understanding AI requires an understanding of the environment.
If an agent’s sensors give it access to the complete state of the environment at every moment, we say that the task environment is fully observable.
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The environment is partially observable due to noise and imprecise sensors, or because the sensor data is simply missing part of the state.
Only one agent participating in an environment is a single agent. More than one agent interacting with the environment is Multi Agent.
If one agent (entity) that maximizes its performance relative to another agent (entity) in an environment is a competitive multi-agent environment. An agent can be treated based on physical laws (behave according to physical laws).
If the next state of the environment is completely determined by the current state and the action taken 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 only from the actions of other agents. Deterministically, the actions of other agents that cannot be predicted by any other agent (every agent).
An environment is uncertain if it is not fully observable or non-deterministic. A non-deterministic environment is one in which actions are characterized by their possible outcomes, but no probabilities are attached to them.
Whereas stochastic generally means that uncertainty about outcomes is quantified in terms of probabilities. Non-deterministic descriptions of the environment are usually associated with measured performance, which requires the agent to succeed in all possible outcomes of its actions.
In an episodic environment, an agent’s experience is divided into atomic episodes. In each episode, the agent receives a perception and then takes one action. The next series does not depend on the actions taken in the previous series. Many classification tasks are episodic.
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In sequential environments, the current decision can affect all future decisions. Episodic environments are much simpler than sequential environments because the agent does not have to think ahead.
A static environment is easy to handle because the agent doesn’t have to look at the world. You don’t have to worry about the passage of time when deciding on an action.
If the environment can change while the agent is deliberating, we say that the agent’s environment is dynamic; otherwise, the environment is static.
A dynamic environment is constantly asking the agent what it wants to do. If it has not yet decided, it is considered a decision to do nothing.
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Semi-dynamic environments: If the environment itself does not change over time, but the agent’s performance measure changes, the environment is semi-dynamic.
These 2 distinct environments refer to the state of the environment, the way time is handled, and the agent’s perceptions and actions. Chess-Discrete, Continuous – Driving an autonomous vehicle.
These two are specific to the environment rather than the agent; If the environment is known, the results of all operations are given. If the environment is unknown, the agent will need 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 are included in reinforcement learning.
The agent is the solution to our problem. The agent needs the intelligence provided by the AI to function in the environment. Each agent must execute its own agent program, agent function, mapping from perception to action. The diagram below describes this.
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Agent program: It runs on some kind of computing device with physical sensors and actuators—call it an architecture.
Therefore, agent = architecture + program; Obviously, the program must match the architecture. An example of the programs operation and architecture is shown below.
The architecture makes sensor perceptions available to the program, runs the program, and feeds the program’s actions to the optional actuators as they are generated. Let’s discuss agent-side concepts.
The structure of the agent program takes current sensing as input from the sensors and returns action to the actuators.
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An agent function that takes the entire perception history as input. The difference between an agent program that takes the current perception as input and an agent function that takes the entire perception history.
There are four basic types of agent programs that embody the principles of almost all intelligent systems. They are simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Each agent program combines certain components in a certain way to generate actions.
It is the simplest agent where these agents directly choose an action from a perception and ignoring the history of the perception. Simple reflex behaviors occur even in more complex environments. Let’s define a condition-action rule.
Conditional Action Rule: This is a connection and is defined as “Processing on visual input to determine a condition, then an action is triggered in the agent program. This connection is called a “condition-action rule”. A schematic diagram of a simple reflex agent defined as follows
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When an agent partially observes the environment, the agent must keep track of the part of the world that it cannot currently see. i.e., the agent must maintain some internal state that depends on the perceptual history and the unobserved aspects of the current state. i.e. pictorially it is defined as
Model: Knowledge of “How the world works,” whether embodied in simple Boolean diagrams or complete scientific theories, is called a model of the world. An agent that uses such a model is called a model-based agent.
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 simply by specifying the destination as the goal. The structure of a goal-based agent is defined as
Goals provide a distinction between happy and unhappy states, denoted by “happy” and “unhappy state”. Specifying happy and unhappy does not mean scientifically, economists and computer scientists use the term “utility”.
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Goal-based agents and utility-based agents have many advantages in terms of flexibility and learning. Utility agents make rational decisions when goals are insufficient 1) The utility function determines an appropriate trade-off. 2) Utility provides a probability of success that can be compared to the importance of goals.
A rational utility-based agent chooses an action that maximizes the expected utility of the action’s outcomes. The structure of the utility-based agent structure is described as follows.
Examples of agents and environments are numerous due to their context, uses, and necessity. Possible and well-known agents and environments listed in the chart below.
Understanding agents and environments is imperative before we design any kind of intelligent agent for our applications. To design any intelligent agent based on the environment, it is necessary to understand what kind of agent to build, what it needs, what kind of equipment, etc., it is better to refer to more chapters 1 and 2 of A. The Modern Approach Stuart Russell, Peter Norvig…
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This article is based on Chapter 2 of Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig. The Wumpus World agent is an example of a knowledge-based agent that represents knowledge representation, reasoning, and planning. A knowledge-based agent relates general knowledge to current perceptions to infer hidden characters of the current state before selecting actions. The need for this is very important in a semi-observable environment.
. Each room is connected to the others via walkways (no rooms are diagonally connected). The knowledge-based agent starts from
. The cave contains some pits, treasures, and a beast named Wumpus. Wumpus can’t move, but eats whatever enters his room. If an agent enters a hole, it gets stuck there. The goal of the agent is to take the treasure and get out of the cave. An agent is rewarded when the target conditions are met. An agent is penalized if it falls into a pit or is eaten by a Wump.
Certain elements help the agent to explore the cave, for example -Wumpus’ adjacent rooms are smelly. -Agent is given a single arrow that it can use to kill a wumpus when faced (Wumpus screams when killed). – The rooms next to the rooms with holes are filled by the wind. – Treasure room
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