Artificial Intelligence

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1 Artificial Intelligence COMP-241, Level-6 Mohammad Fahim Akhtar, Dr. Mohammad Hasan Department of Computer Science Jazan University, KSA Chapter 2: Intelligent Agents In which we discuss the nature of agents, perfect or otherwise, the diversity of environments, and the resulting menagerie of agent types. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. A human agent has eyes, ears, and other organs for sensors and hands, legs, mouth, and other body parts for actuators. A robotic agent might have camera and infrared range finders for sensors and various motors for actuators. A software agent receives key strokes, file contains, and network packets as sensory inputs and acts on the environments by displaying on the screen, writing files, and sending network packets. We will make the general assumption that every agent can perceive its own actions. An agent choice of action at any given instant can depend on the entire percept sequence observed to date. An agent s behavior is described by the agent function that maps any given percept sequence to an action. Intelligent agents are supposed to maximize their performance measure. Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 1 of 10

2 The following are the diagram of agents interact with environments through sensors and actuators: Good Behavior: The concept of Rationality Rational Agent An agent should strive to "do the right thing" based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful. For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. Performance Measure The criterion for success of an agent s behavior. When an agent is plunked down in an environment, it generates a sequence of actions according to the percepts it receives. As a general rule, it is better to design performance measures according to what one actually wants in the environment, rather than according to how one thinks the agent should behave. Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 2 of 10

3 Vacuum Cleaner Agent Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 3 of 10

4 Demonstrate program in Lisp: The nature of environments PEAS Performance Environments Actuators Sensors Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 4 of 10

5 The structure of agents Agent = architecture + program Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 5 of 10

6 Types of Intelligent Agents 1. Simple Reflex Agents This the simplest type of agent architecture possible. The underlying concept is very simple and lacks much intelligence. For each condition that the agent can observe from its sensors based on the changes in the environment in which it is operating in, there is a specific action(s) defined by an agent program. So, for each observation that it receives from its sensors, it checks the condition-action rules and find the appropriate condition and then performs the relevant action defined in that rule using its actuators. This can only be useful in the cases thatt the environment in fully observable and the agent program contains all the condition-action rules possible for each observance, which is somewhat not possible in real world scenarios and only limited to toy simulation based problems. The given below figure shows the concept of simple reflex type agent. Figure Simple reflex agent In the above case, the agent program contains a look-up table which has been constructed prior to the agent being functional in the specific environment. The look-up table should consist of all possible percept sequence mappings to respective actions. Thus based on the input that the agent receives via the sensors (about the current state of the environment), the agent would access this look-up table and retrieve the respective action mapping for that percept sequence and inform its actuators to perform that action. This process is not very effective in a scenario where the environment is constantly changing while the agent is taking the action because, the agent is acting on a percept sequence that it acquired previously to the rapid change in the environment and therefore the performed action might not suit the environment s state after the change. Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 6 of 10

7 2. Model based reflex agents This is a more improved version of the first type of agents with the capability of performing an action based on how the environment evolves or changes from the current state. As in all agent types, model based reflex agents also acquire the percepts about the environment through its sensors. These percepts would provide the agent with the understanding of what the environment is like now at that moment with some limited facts based on its sensors. Then the agent would update the internal state of the percept history and thus would yield some unobserved facts about the current state of the environment. To update the internal state, information should exist about how the world (environment) evolves independently of the agent s actions and information about how the agent s actions eventually affect the environment. This idea about incorporating the knowledge of evolvement of the environment is known as a model of the world. This explains how the name model based was used for this agent type. Figure Model based reflex agent Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 7 of 10

8 The above diagram shows the architecture of a model based reflex agent. Once the current percept is received by the agent through its sensors, the previous internal state stored in its internal state section in connection with the new percepts determines the revised description about the current state. Therefore, the agent function updates its internal state every time it receives a new percept. Then based on the new updated percept sequence based on the look-up table s matching with that entry would determine what action needs to be performed and inform the actuators to do so. 3. Goal Based Agents This agent is designed so that it can perform actions in order to reach a certain goal. In any agent the main criteria is to achieve a certain objective function which can in layman s terms referred to as a goal. Therefore, in this agent type goal information is defined so that is can determine which suitable action or actions should be performed out of the available set of actions in order to reach the goal effectively. For example, if we are designing an automated taxi driver, the destination of the passenger (which would be fed in as a goal to reach) would provide him with more useful insight to select the roads to reach that destination. Here the difference with the first two types of agents is that it does not have hard wired condition-action rule set thus the actions are purely based on the internal state and the goals defined. This sometimes might lead to less effectiveness, when the agent does not explicitly know what to do at a certain time but it is more flexible since based on the knowledge it gathers about the state changes in the environment, it can modify the actions to reach the goal. Figure Goal based agent Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 8 of 10

9 4. Utility Based Agent Goals by themselves would not be adequate to provide effective behavior in the agents. If we consider the automated taxi driver agent, then just reaching the destination would not be enough, but passengers would require additional features such as safety, reaching the destination in less time, cost effectiveness and so on. So in order to combine goals with the above features desired, a concept called utility function is used. So based on the comparison between different states of the world, a utility value is assigned to each state and the utility function would map a state (or a sequence of states) to a numeric representation of satisfaction. So, the ultimate objective of this type of agent would be to maximize the utility value derived from the state of the world. The following diagram depicts the architecture of a Utility based agent. Figure Utility based agent Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 9 of 10

10 Exercises: Q1. Define the following terms with suitable example: a) Agent b) Intelligent Agent c) Rational Agent d) Performance e) Environment f) Actuators g) Sensors Q2. Write the PEAS description of the following agent: a) Taxi Driver b) Medical Diagnosis System c) Satellite Image Analysis System d) Part Picking Robot e) Refinery Controller f) Interactive English Tutor g) ATM (Automated Teller Machine) h) BANK i) Robot Soccer Player j) Internet Book shopping agent k) Autonomous Mars rover l) Mathematician s theorem proving assistant Q3. Explain the function of simple reflex agent with suitable neat diagram. Q4. Write the function of reflex vacuum agent. Q5. Write the algorithm of simple reflex agent. Q6. Write the algorithm of model based reflex agent. Q7. Explain the function of model based reflex agent with suitable neat diagram. Q8. Explain the function of goal based agent with suitable neat diagram. Q9. Explain the function of utility based agent with suitable neat diagram. References: Artificial Intelligence A modern Approach, Second edition, Stuart Russell & Peter Norvig Developed BY: Mohammad Fahim Akhtar Version 1.4 Page 10 of 10

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