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Artificial Intelligence

by Anna Superkings
Type: NoteInstitute: ANNA UNIVERISTY Specialization: Information Technology EngineeringDownloads: 5Views: 78Uploaded: 15 days agoAdd to Favourite

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Anna Superkings
Anna Superkings
Artificial Intelligence It is the study of how to make computers do things which, at the moment, people do better. It leads FOUR important categories. i) Systems that think like humans ii) ii) Systems that act like humans iii) Systems that think rationally iv) iv) Systems that act rationally Acting humanly: The Turing Test approach: To conduct this test, we need two people and the machine to be evaluated. One person plays the role of the interrogator, who is in a separate room from the compute and the other person. The interrogator can ask questions of either the person or the computer by typing questions and receiving typed responses. However, the interrogator knows them only as A and B and aims to determine which the person is and which are the machine. The goal of the machine is to fool the interrogator into believing that is the person. If the machine succeeds at this, then we will conclude that the machine is acting humanly. But programming a computer to pass the test provides plenty to work on, to posses the following capabilities. Thinking Humanly: The Cognitive modeling approach: To construct a machine program to think like a human, first it requires the knowledge about the actual workings of human mind. After completing the study about human mind it is possible to express the theory as a computer program. It the program‘s input/output and timing behavior matches with the human behavior then we can say that the program‘s mechanism is working like a human mind. Example: General Problem Solver (GPS) Thinking rationally: The laws of thought approach: The right thinking introduced the concept of logic. Example: Ram is a student of III year CSE. All students are good in III year in CSE. Ram is a good student ACTING RATIONALLY: Acting rationally means, to achieve one‘s goal given one‘s beliefs. In the previous topic laws of thought approach, correct inference is selected, conclusion is derived, but the agent acts on the conclusion defined the task of acting rationally. AGENT An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. A rational agent is one that does the right thing. CS6659 & Artificial Intelligence Unit I Page 1
We use the term performance measure for the how—the criteria that determine how MEASURE successful an agent is. Obviously, there is not one fixed measure suitable for all agents agent program: a function that implements the agent mapping from percepts to actions. We assume this program will run on some sort of ARCHITECTURE( computing device) . Hence A Agent is a combination of Architecture and Program. agent = architecture + program The Skeleton of an agent is function SKELETON-AGENT( percept) returns action static: memory, the agent’s memory of the world memory <-UPDATE-MEMORY(memory, percept) action <-CHOOSE-BEST-ACTION(memory) memory <-UPDATE-MEMORY(memory, action) return action A Table driven Agent function: function TABLE-DRIVEN-AGENT( percept) returns action static: percepts, a sequence, initially empty table, a table, indexed by percept sequences, initially fully specified append percept to the end of percepts action <-LOOKUP( percepts, table) return action Four types of agent program: 1. Simple reflex agents 2. Agents that keep track of the world 3. Goal-based agents 4. Utility-based agents Simple reflex agents: Structure of a simple reflex agent in schematic form, showing how the condition–action rules allow the agent to make the connection from percept to action. CS6659 & Artificial Intelligence Unit I Page 2
function SIMPLE-REFLEX-AGENT( percept) returns action static: rules, a set of condition-action rules state <-INTERPRET-INPUT( percept) rule <-RULE-MATCH(state, rules) action <-RULE-ACTION[rule] return action CS6659 & Artificial Intelligence Unit I Page 3
2.Agents that keep track of the world(Reflex Model) The simple reflex agent described before will work only if the correct decision can be made on the basis of the current percept. In order to choose an action sometimes INTERNAL STATE will help to take good decision. function REFLEX-AGENT-WITH-STATE( percept) returns action static: state, a description of the current world state rules, a set of condition-action rules state <-UPDATE-STATE(state, percept) rule <-RULE-MATCH(state, rules) action <-RULE-ACTION[rule] state <-UPDATE-STATE(state, action) return action 3. Goal-based agents Knowing about the current state of the environment is not always enough to decide what to do. In other words, as well as a current state description, GOAL the agent needs some sort of goal information, which describes situations that are desirable. 4. Utility Based Model: Goals alone are not really enough to generate high-quality behavior. For example, there are many action sequences that will get the taxi to its destination, thereby achieving the goal, but some are quicker, safer, more reliable, or cheaper than others. Goals just provide a crude distinction between “happy” and “unhappy” states, whereas a more general performance measure should allow a comparison of different world states. Then it has higher utility for the agent CS6659 & Artificial Intelligence Unit I Page 4

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