Artificial Intelligence Lecture 1

Objectives Definition Foundation of AI History of AI Agent Application of AI

Definition It is the science and engineering of making intelligent machines, especially intelligent computer programs It is related to the similar task of using computers to understand human intelligence

Definition of scientists

Foundation Strong AI Introduced by John Searle in 1980  Minds,Brains and Programs(Article) Deals with the creation of computer-based artificial intelligence  truly reason and solve problems Types  Human-like AI(computer program think and reason just like human mind), Non-Human-like AI( computer programs develops non-human way of thinking and reasoning)

Foundation(Cont..) Weak AI Deals with the creation of some form of artificial intelligence that can reason and solve problems in a limited

History of AI To program computers think like human, at first we have to resolve how humans think Studied on human-thinking and mind go back nearly 2000 years Arostotle was the first philosopher who formalize human thinking Warren McCulloch and Walter Pitts(1943)  Models of Artificial Neurons  earliest work in AI Marvin Minsky and Dean Edmond(1951)  first Neural computer(SNARC) McCarthy wrote LISP(1958)

Application of AI Chinook(1994)  man-machine champion in checker Deep Blue Chess playing computer beat Garry Kasparov in 1996 Fuzzy Logic is a technique for reasoning under uncertainty Expert systems Neural Network

Agent An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors human agent has eyes, ears as sensor and hands,legs as effectors a hardware or software based computer system that has the following properties Autonomy  operates without direct intervention of human Social ability  Agents can communicate with other agents via an agent communication language Reactivity  Agents perceive their environment

Architecture of Agent

Types of Agent Rational agent  does the right thing Omniscient agent  knows the actual outcome of its actions

Some terms Performance measure: a subjective measure to characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.) (degree of) Autonomy: to what extent is the agent able to make decisions and take actions on its own?

Structure of Intelligent Agents

Types of agent program simple reflex agents model-based reflex agents goal-based agents utility-based agents

Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition- action rule: if condition then action.

Model-based reflex agents It works by finding a rule whose condition matches the current situation (as defined by the percept and the stored internal state) and then doing the action associated with that rule

Goal-based agents Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information. Goal information describes situations that are desirable. allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state

Utility-based agents Goal-based agents only distinguish between goal states and non-goal states to define a measure of how desirable a particular state is can be obtained through the use of a utility function which maps a state to a measure of the utility of the state

Mobile agents Programs that can migrate from one machine to another. Execute in a platform-independent execution environment Two types: – One-hop mobile agents (migrate to one other place) – Multi-hop mobile agents (roam the network from place to place) Applications: – Distributed information retrieval. – Telecommunication network routing.

Properties of environments Accessible Deterministic Episodic Static Discrete

Accessible vs. inaccessible If an agent's sensory apparatus gives it access to the complete state of the environment  accessable

Deterministic vs. nondeterministic If the next state of the environment is completely determined by the current state and the actions selected by the agents, then we say the environment is deterministic

Episodic vs. nonepisodic In an episodic environment, the agent's experience is divided into "episodes." Each episode consists of the agent perceiving and then acting. The quality of its action depends just on the episode itself, because subsequent episodes do not depend on what actions occur in previous episodes. Episodic environments are much simpler because the agent does not need to think ahead.

Static vs. dynamic If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise it is static.

Discrete vs. continuous If there are a limited number of distinct, clearly defined percepts and actions we say that the environment is discrete. Chess is discrete—there are a fixed number of possible moves on each turn. Taxi driving is continuous—the speed and location of the taxi and the other vehicles sweep through a range of continuous values

Questions Define AI according to different scientists Distinguish between strong AI and weak AI Define agent State properties of agent Explain structure of agent with proper example Mention differences between rational and omniscient agent How is an Agent different from other software? Explain different types of agent program Mention properties of environment