artificial intelligence best presentation.pptx
- 1. CHAPTER : 2 Intelligent Agents 1 Outlines:- Agent and Environment Different types of agent The agents PEAS farmwork Structure of agent Types of agent program.
- 2. Agents and Environments An agent: is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators/effectors. Figure 1:Agents interact with environments through sensors and effectors. 2
- 3. Con’t… A human agent: has eyes, ears, and other organs for sensors and hands, legs, vocal tract, and so on for actuators. A robotic agent: might have cameras and infrared range finders for sensors and various motors for actuators. A software agent: receives keystrokes, file contents, and network packets as sensory inputs and acts on the environment by displaying on the screen, writing files, and sending network packets. 3
- 4. Intelligent agents An intelligent agent: is a system that perceives its environment, learns from it, and interacts with it intelligently. Intelligent agents can be divided into two broad categories: software agents and physical agents. 4
- 5. Software agent A software agent is a set of programs that are designed to do particular tasks. For example, a software agent a search engine used to search theWorldWideWeb and find sites that can provide information about a requested subject. 5
- 6. Physical agent A physical agent (robot) is a programmable system that can be used to perform a variety of tasks. Simple robots can be used in manufacturing to do routine jobs such as assembling, welding, or painting. Some organizations use mobile robots that do routine delivery jobs such as distributing mail or correspondence to different rooms. 6
- 7. Acting of Intelligent Agents (Rationality) 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. Performance measure: An objective criterion for success of an agent's behavior. 7
- 8. Con’t… E.g. performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc. Rational Agent: 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. 8
- 9. Con’t… Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) 9
- 10. Con’t… In summary what is rational at any given point depends on PEAS (Performance measure, Environment, Actuators, Sensors) framework. Performance measure The performance measure that defines degrees of success of the agent 10
- 11. Con’t… Environment Knowledge:What an agent already knows about the environment Actuators – generating actions The actions that the agent can perform back to the environment Sensors – receiving percepts Perception: Everything that the agent has perceived so far concerning the current scenario in the environment 11
- 12. Example: PEAS Consider the task of designing an automated taxi driver agent: Performance measure: Safe, fast, legal, comfortable trip, maximize profits. Environment: Roads, other traffic, pedestrians, customers Actuators: Artificial legs & hands, Speaker Sensors: Cameras, GPS, engine sensors, recorder (microphone) Goal: driving safely from source to destination point Write the PEAS by considering that you are designing the automated waiter in hotel agent? 12
- 13. Structure of agents Agent = architecture + program Architecture = some sort of computing device (sensors + actuators) (Agent) Program = some function that implements the agent mapping. Agent Program = Job ofAI 13
- 14. Agent programs Input for Agent Program Only the current percept. Input for Agent Function The entire percept sequence. The agent must remember all of them. Implement the agent program as A look up table (agent function) 14
- 15. Types of agent programs 1. Simple ReflexAgents 2. Model-Based ReflexAgent 3. Goal based agents 4. Utility based agents 5. LearningAgents 15
- 16. 1. Simple Reflex Agents It works by finding a rule whose condition matches the current situation (as defined by the percept) and then doing the action associated with that rule. These agents select actions on the basis of the current percept, ignoring the rest of the percept history. It uses just condition-action rules The rules are like the form “if … then …” Because knowledge sometimes cannot be stated explicitly Work only if the environment is fully observable 16
- 17. Con’t… Example:Automated taxi driving agent. If the car in front brakes and its brake lights on, then you should notice this and initiate braking.This can be written as condition-action rule: If car-in-front-is-braking then initiate-braking; Humans also have many such connections, some of which are learned responses (as for driving) and some of which are innate reflexes (such as blinking when something approaches the eye). 17
- 18. Structure of a simple reflex agent 18
- 19. Con’t… Limitation of simple reflex agent: The agent will work only if the environment is fully observable that is, it will work only if the correct decision can be made on the basis of only the current percept. Even a little bit of unobservability can cause serious trouble. 19
- 20. 2. Model-Based ReflexAgent It works by finding a rule whose condition matches the current situation/state. For the world that is partially observable The agent has to keep track of an internal state That depends on the percept history Reflecting some of the unobserved aspects E.g. driving a car and changing track Requiring two types of knowledge How the world evolves independently of the agent How the agent’s actions affect the world 20
- 21. Con’t… Note: Regardless of the kind of representation used, it is seldom possible for the agent to determine the current state of a partially observable environment exactly. It can do only “best guess”. Example: An automated taxi may not be able to see around the large truck that has stopped in front of it and can only guess about what may be causing the hold-up. 21
- 22. Structure of Model-Based reflex agent 22
- 23. 3. Goal based agents Knowing something about the current state of the environment is not always enough to decide what to do. Example: At a road junction, the taxi can turn left, turn right, or go straight on.The correct decision depends on where the taxi is trying to get it. The goal is another issue to achieve Judgment of rationality / correctness 23
- 24. Structure of a Goal-based agent 24
- 25. Con’t… Conclusion Goal-based agents are less efficient but more flexible, because the knowledge that supports its decision is represented explicitly and can be modified. Agent Different goals different tasks Search and planning Two other sub-fields in AI To find out the action sequences to achieve its goal 25
- 26. 4. Utility based agents Goals alone are not enough to generate high-quality behavior in most environments. Example: Many action sequences will get the taxi to its destination (thereby achieving the goal) but some are quicker, safer, more reliable, or cheaper than others. Many action sequences the goals Some are better and some worse If goal means success. Then utility means the degree of success (how successful it is) 26
- 27. Con’t… It is said state A has higher utility If state A is more preferred than others Utility is therefore a function That maps a state onto a real number The degree of success 27
- 28. Structure of a utility-based agent 28
- 29. 5. Learning Agents After an agent is programmed, can it work immediately? No, it still need teaching InAI Once an agent is done We teach it by giving it a set of examples Test it by using another set of examples We then say the agent learns A learning agent 29
- 30. Con’t… Four conceptual components Learning element: Responsible for making improvements. Performance element: Responsible for selecting external actions. Critic: Tells the Learning element how well the agent is doing with respect to fixed performance standard. Problem generator: Suggest actions that will lead to new and informative experiences. 30
- 32. THANK YOU Q & A ? 32
- 33. Quiz one 5% 1. For what purpose an agent uses sensor and actuator? 2. Do you think that are you an intelligence? If you say yes or No? Why and what are the characteristics of you? 3. What is a difference b/n Simple Reflex Agents and Model-Based Reflex Agent? 4. From types of agent programs which one better ? Why/how ? 5. Consider that you are designing an intelligent agent for candidate being employed in an organization, write the PEAS of the agent? 33