Particularly useful when i there are conflicting goalsThe reflex agents are known as the simplest agents because they directly map states into actionsUnfortunately, these agents fail to operate in an environment where the mapping is too large to store and learn Goalbased agent, on the other hand, considers future actions and the desired outcomes Here, we will discuss one type of goalbased agent known as a problemsolving agentUtilities indicate preferences among states;
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Goal based agent example-The goal based agent focuses only on reaching the goal set and hence the decision took by the agent is based on how far it is currently from their goal or desired state Their every action is intended to minimize their distance from the goal This agent is more flexible, and the agent develops its decision making skill by choosing the rightUtility Based Agent Determines the best way to reach the goal Learning Agent Analyzes information to make improvements 26) This exercise explores the differences between agent functions and agent programs A) Can there be more than one agent program that implements a given agent function?
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How the world is affected by the agents actions Eg If our mars Lander took a sample under a precarious ledge it could displace a rock and it could be crushed We can predict how the world will react with facts like if you remove a supporting rock under a ledge the ledge will fall, such facts are called models, hence the name modelbased agentQuestion 4 For each of the four main types of agent Simple reflex agents, Reflex agents with an internal state, Goal based agents, and Utility based agents For example, they represent the interaction of a Simple reflex agent with its environment as Try to come up with alternative/better ways of representing those four types of agentCO2 2 a) Explain the goalbased agent with an example and specify its task 5 environment CO2 b) Briefly explain how you can solve a 4queen problem using a local search 5 On ;
GoalBased Agent Contains some sort of goal information and knowledge about the results of possible actions performs the action or action sequence that achieves the goals;Agentbased modeling relies on simulating the actions and interactions of autonomous agent s to evaluate their effects on the system It is often used to predict the projections that we will obtain given a complex phenomena The main purpose is to obtain explanatory insight on how the agents will behave given a particular set of rulesGoalBased Agent 19 Choose actions so as to achieve a (given or computed) goal A goal is a description of a desirable situation Keeping track of the current state is often not enough need to add goals to decide which situations are good Deliberative instead of reactive May have to consider long sequences of possible actions before deciding
Question CO2 2 a) Explain the goalbased agent with an example and specify its task 5 environmentFunction MODELGOALBASEDAGENT(percept) returns an action persistent state , what the current agent sees as the world state model , a description detailing how theGoal Based Agent En vi Sensors What it will be like if I do action A State How the world evolves What my actions do What the world is like now CISC4/681 Introduction to Artificial Intelligence 29 Agent ronment What action I should do now Goals Actuators UtilityBased Agent En vi Sensors What it will be like
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Occasionally , goal based action selection is straightforward (eg follow the acti on that leads directly to the goal);Agent Frameworks GoalBased Agents 1 Agent Sensors Effectors Goals What action I should do now Environment State How world evolves What my actions do What world is like now What it will be like if I do action A Agent Frameworks GoalBased Agents 2 Implementation and Properties • Instantiation of generic skeleton agent Figure 211Our goal is to pick up every thing on that list This makes it easier to decide if you need to choose between milk and orange juice because you can only
Section 02
Section 02
Goalbased agent program function GOALBASEDAGENT(percept) returns an action persistent state, the agent's current conception of the world state goal, a description of what the agent would like to achieve rules, a set of conditionaction rules action, the most recent action, initially none3 Goal – based agents 4 Utility – based agents 1 Simple reflex agents These agents select actions on the basis of the current percept, ignoring the rest of the percept history Example The vacum agent whose agent function is tabulated in figure (3) is a simple reflex agent, because its decision is based only on the current locationGoalbased agents that use more advancedfactored or structured representations are usually called planning agents and are discussed in Chapter 7 and 11 We start our discussion of problem solving by defining precisely the elements that constitute a "problem" and its "solution," and give several examples to illustrate these definitions
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UtilityBased Agent Goals designate desired states;Link for Simple reflex agents https//wwwyoutubecom/watch?v=KZFfbebQPAU&t=218sLink for Model Based Agents https//wwwyoutubecom/watch?v=xKxh3fQwU8E&t=1Goal based agents In life, in order to get things done we set goals for us to achieve, this pushes us to make the right decisions when we need to A simple example would be the shopping list;
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3Goalbased agents An agent knows the description of current state and also needs some sort of goal information that describes situations that are desirable The action matches with the current state is selected depends on the goal state The goal based agent is more flexible for more than one destination alsoFor an example of a nongoal based utility agent consider a form of a partisan sudoku in which players compete to control regions on the gameboard by placement of weighted integers In a game with 9 regions, the goal based agent seeks to control a specific number of regions at the end of playIf the agent is conservative, the goal might be 5 regionsLearning Agent Simple reflex agents Simple reflex agents ignore the rest of the percept history and act only on the basis of the current percept Percept history is the history of all that an agent has perceived to date The agent function is based on the conditionaction
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UtilityBased Agents These agents are almost like the goalbased agent but provide an additional component of utility measurement which makes them different by providing a measure of success at a given stateUtilitybased agent act based not only goals but also the simplest thanks to achieving the goal The Utilitybased agent is beneficial when there areGoal based agents usually less efficient but more flexible than reflexbased agents A goal basedagent can suit itself based on the environment For example, a goalbased agent can adapt its behavior based on the sensor data 4 UtilityBased AgentsUtilitybased agents These types of agents are concerned about the performance measure The agent selects those actions which maximize the performance measure and devote towards the goal Example The main goal of chess playing is to 'checkandmate' the king, but the player completes several small goals previously Note Utilitybased
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Utilitybased agents the agent is aware of a utility function that estimates how close the current state is to the agent's goal Learning Agents Agents capable of acquiring new competence through observations and actions Components learning element (modifies the performance element) performance element (selects actions) feedback elementCPE/CSC 580S06 Artificial Intelligence – Intelligent Agents ProblemSolving Agents Subclass of goalbased agents goal formulation problem formulation example problems • toy problems • realworld problems search • search strategies • constraint satisfaction solutionUtilitybased agents Sometimes achieving the desired goal is not enough We may look for quicker, safer, cheaper trip to reach a destination Agent happiness should be taken into consideration We call itutility A utility function is the agent's performance measure Because of the uncertainty in the world, a utility agent choses
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Goal Based Agents
Goal Based Reflex Agent # Artificial Intelligence Online Course Lecture 6At other times, however, the agent must consider also search and planning Decision making of this latter kind involves consideration of the future Goal based agents are commonly more flexible than reflex agentsIn artificial intelligence, an intelligent agent (IA) is anything which perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or may use knowledgeThey may be simple or complex — a thermostat is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm
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An utilitybased reflex agent is like the goalbased agent but with a measure of how much happy an action would make it rather than the goalbased binary feedback 'happy', 'unhappy' This kind of agents provide the best solution An example is the route recommendation system which solves the 'best' route to reach a destinationIntelligent agent On the Internet, an intelligent agent (or simply an agent ) is a program that gathers information or performs some other service without your immediate presence and on some regular schedule Typically, an agent program, using parameters you have provided, searches all or some part of the Internet, gathers information you'reAnswer (1 of 3) Goal and utility could be considered ways of defining desire and happiness in intelligent agents enwikipediaorg/wiki/Intelligent_agent#Goal
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Goalbased agents and Utilitybased agents has many advantage 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) Utility provides likelihood of success can be weighted against the importance of the goalsExample Goalbased agents Chess playing robot Taxidriving robot Can blur the lines a little Simple mail delivery robot that follows a set route More robust mail delivery robot that can replan route to handle obstacles 0435 UtilityBased Agent May be many action sequences that achieve a goalGOAL is an agent programming language for programming cognitive agentsGOAL agents derive their choice of action from their beliefs and goals The language provides the basic building blocks to design and implement cognitive agents by programming constructs that allow and facilitate the manipulation of an agent's beliefs and goals and to structure its decisionmaking
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GoalBased Agents Previously we discussed ModelBased Reflex Agents as a way to design simple enemies We considered a very simple behavior of the AI enemy which can be stated in the form of following conditionaction rules If patrolling and no enemy in sight then Patrol predefined path If patrolling and enemy in sight, switch mode fromAn intelligent agent may learn from the environment to achieve their goals A thermostat is an example of an intelligent agent Following are the main four rules for an AI agent Rule 1 An AI agent must have the ability to perceive the environment Rule 2 The observation must be used to make decisions Rule 3 Decision should result in an actionSee Fig 211 in text;
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An improvement over goal based agents, helpful when achieving the desired goal is not enough We might need to consider a cost For example, we may look for quicker, safer, cheaper trip to reach a destination This is denoted by a utility function A utility agent will chose the action that maximizes the expected utilityGive an example, or show why one is not possibleA goalbased agent takes it a step further by using a goal in the future to help make decisions about how best to reach that outcome It uses a specific method known as search and planning
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An example of a goalbased agent A method that a goalbased agent uses to arrive at its goal The concept of targeting a goal and determining the correct actions that are needed to reach itGoalbased agents Knowing about the current state of the environment is not always enough to decide what to do For example, at a road junction, the taxi can turn left, right, or go straight on The right decision depends on where the taxi is trying to get to In other words, as well as a current state description, the agent needs some sort of
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