Artificial Intelligence

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 • Introducedby 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 subjectivemeasureto 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

Artificial Intelligence