Artificial Intelligence

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1. Introduction What is artificial intelligence? 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, but AI does not have to confine itself to methods that are biologically observable. What is intelligence? Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Natural Intelligence •

Definition. Intelligence – inter ligare (Latin) – the capacity of creating connections between notions.

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Wikipedia: the ability to solve problems.

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WordNet: the ability to comprehend; to understand and profit from experience.

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Complex use of creativity, talent, imagination.

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Biology - Intelligence is the ability to adapt to new conditions and to successfully cope with life situations.

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Psychology - a general term encompassing various mental abilities, including the ability to remember and use what one has learned, in order to solve problems, adapt to new situations, and understand and manipulate one’s reality.

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Nonlinear, non-predictable behavior.

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Dictionary: Intelligence 1. (a) The capacity to acquire and apply knowledge. (b) The faculty of thought and reason. (c) Superior powers of mind. 2. An intelligent, incorporeal being, especially an angel. 3. Information; news. 4. (a) Secret information, especially about an actual or potential enemy. (b) An agency, staff, or office employed in gathering such information. (c) Espionage agents, organizations, and activities considered as a group What is intelligence then? Fast thinking? Knowledge? Ability to pass as a human? Ability to reason logically? Ability to learn? Ability to perceive and act upon one’s environment? Ability to play chess at grand-master’s level? Why Study AI? AI helps computer scientists and engineers build more useful and user-friendly computers, Psychologists, linguists, and philosophers understand the principles that constitute what we call intelligence. AI is an interdisciplinary field of study. Many ideas and techniques now standard in CS (symbolic computation, time sharing, objects, declarative programming, . . . ) were pioneered by AI-related research.

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Dictionary: Artificial Intelligence 1. Dictionary 1: (a) The ability of a computer or other machine to perform those activities that are normally thought to require intelligence. (b) The branch of computer science concerned with the development of machines having this ability. 2. Dictionary 2: The subfield of computer science concerned with the concepts and methods of symbolic inference by computer and symbolic knowledge representation for use in making inferences. AI can be seen as an attempt to model aspects of human thought on computers. It is also sometimes defined as trying to solve by computer any problem that a human can solve faster. Definition from R & N book: a program that –

Acts like human (Turing test)

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Thinks like human (human-like patterns of thinking steps)

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Acts or thinks rationally (logically, correctly)

Definition: The science of developing methods to solve problems usually associated with –

human intelligence. Alternate definitions: –

building intelligent entities or agents;

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making computers think or behave like humans

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studying the human thinking through computational models;

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generating intelligent behavior, reasoning, learning.

Ray Kurzweil on AI � –

“Artificial intelligence is the ability to perform a task that is normally performed by natural intelligence, particularly human natural 3

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intelligence.” � (or in some cases, tasks that require greater-than-human intelligence) John McCarthy on AI � “It is the science and engineering of making intelligent machines, especially intelligent computer programs.” � “Intelligence is the computational part of the ability to achieve goals in the world.” Operational Definition of AI Systems that act like humans Turing test. Systems that think like humans Cognitive Science Systems that think rationally Logic-based AI Systems that act rationally Rational Agents Thinking Rationally: Laws of Thought Several Greek schools at the time of Aristotle developed various forms of logic: Notation and rules of derivation for thoughts; they may or may not have proceeded to the idea of mechanization Direct line through mathematics and philosophy to modern AI Problems: 1. Not all intelligent behavior is mediated by logical deliberation 2. What is the purpose of thinking? What thoughts should I have? Thinking Humanly: Cognitive Science 1960s “cognitive revolution”: information-processing psychology replaced prevailing orthodoxy of behaviorism

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Require scientific theories of internal activities of the brain What level of abstraction? “Knowledge” or “circuits”? How to validate? It requires 1. Predicting and testing behavior of human subjects (top-down) 2. Direct identification from neurological data (bottom-up) Both approaches, Cognitive Science and Cognitive Neuroscience, share with AI on: the available theories do not explain (or engender) anything resembling human-level general intelligence. Rational Agents An agent is an entity that perceives and acts This course is about designing rational agents Abstractly, an agent is a function from percept histories to actions: For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Caveat: computational limitations make perfect rationality unachievable Approach: design best program for given machine resources Acting Rationally Rational behavior: doing the right thing, that which is expected to maximize goal achievement, given the available information Doesn’t necessarily involve thinking—e.g., blinking reflex—but thinking should be in the service of rational action Aristotle: Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good.

Artificial Intelligence Vs Conventional Programming

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Artificial Intelligence

Conventional Programming

Artificial Intelligence a. primarily symbolic b. heuristic search (solution steps implicit) c. control structure usually separate from domain knowledge d. usually easy to modify, update and enlarge e. some incorrect answers often tolerable f. satisfactory answers usually

Conventional computer programming a'. algorithmic (solutions steps explicit) b'. primarily numeric c'. information and control integrated together d'. difficult to modify e'. correct answers required f'. best possible solution usually sought

acceptable

Foundations of AI (a) Computer Science & Engineering Computer hardware and Software (b) Philosophy Rule of Reasoning (c) Biology Human /animals brain activity (d) Linguistics communication (e) Cognitive Science High level human/animal thinking (f) Psychology Complex systems game (g) Economics Cost benefits ratio (h) Mathematics

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Logic algorithm optimization AI Prehistory Philosophy logic, methods of reasoning mind as physical system foundations of learning, language, rationality Mathematics formal representation and proof algorithms, computation, (un)decidability, (in)tractability probability Psychology adaptation phenomena of perception and motor control experimental techniques (psychophysics, etc.) Economics formal theory of rational decisions Linguistics knowledge representation grammar Neuroscience plastic physical substrate for mental activity Control theory homeostatic systems, stability simple optimal agent designs History of AI •

The birth of AI (1943 – 1956) –

Pitts and McCulloch (1943): simplified mathematical model of neurons (resting/firing states) can realize all propositional logic primitives (can compute all Turing computable functions)

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Allen Turing: Turing machine and Turing test (1950)

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Claude Shannon: information theory; early game theory, possibility of chess playing computers

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Tracing back to Boole, Aristotle, Euclid (logics, syllogisms, algebra of symbols)

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Early enthusiasm (1952 – 1969) –

1956 Dartmouth conference

John McCarthy (Lisp); Marvin Minsky (first neural network machine); 7

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Alan Newell and Herbert Simon (GPS); –

Emphasize on intelligent general problem solving

Heuristics of human problem solving (means-ends analysis in GPS ); Resolution by John Robinson (basis for automatic theorem proving); heuristic search (A*, AO*, game tree search) •

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Emphasis on knowledge (1966 – 1974) –

domain specific knowledge is the key to overcome existing difficulties

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knowledge representation (KR) paradigms

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declarative vs. procedural representation

Knowledge-based systems (1969 – 1979) –

DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical compounds)

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MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases)

EMYCIN: an ES shell –

PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits)

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AI became an industry (1980 – 1989) –

wide applications in various domains

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commercially available tools

Current trends (1990 – present) –

more realistic goals

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more practical (application oriented)

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resurgence of neural networks and emergence of genetic algorithms

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distributed AI, intelligent agents, and semantic web

Possible Approaches

Like humans

Thin k

GPS

Ac t

Turing test, Eliza

Well Rationa l agents Heuristi c systems

AI tends to work mostly in this area

Planning and Problem Solving In the AI literature, planning refers to determining a sequence of actions you know how to perform that will achieve a particular objective. Problem solving is finding a plan for a task in an abstract domain. A problem is hard if you do not know how to work out an appropriate sequence of steps and is solved once such a sequence has been found: actual execution is irrelevant. Unlike many areas of AI, planning shows a clear line of researchers building on each other's work

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I. SOME BASIC IDEAS To avoid having to cope with the complexities of the physical world, much early work in AI was directed toward abstract activities such as proving theorems, playing games like chess and checkers, or solving puzzles. To illustrate the discussion in this section, we will use the Tower of Hanoi (Hanoi), a puzzle involving three pegs, on which disks can be placed, and a set of disks of varying size

The disks only can be moved, one at a time, between the pegs, and a disk must never be stacked on top of a smaller one. The problem is to transport the entire stack of disks to another peg. Reasoning about such a problem obviously requires representing states of the world and having some way of specifying the objective, or goal. These representations must be rich enough to embody all the aspects of the world that will be reasoned about. In particular, since planning is about changing things, every property that might be affected must be represented as dependent on time in some way. For Hanoi, this requires only an ability to represent sets of disks' positions as the disks are initially, as they are required to be eventually, and as they may be in between. A planner also needs to represent what can be done—such as the moves that can be made, as determined by the nature of the game or puzzle. There is a fundamental difference between an agent executing an action, and thus affecting the world, and a planning system manipulating representations to derive information about doing so, which we call applying an operator Introduction to Predicate calculus

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The Propositional Calculus Symbols and Sentences The propositional calculus and, in the next subsection, the predicate calculus are first of all languages. Using their words, phrases, and sentences, we can represent and reason about properties and relationships in the world. The first step in describing a language is to introduce the pieces that make it up: its set of symbols. PROPOSITIONAL CALCULUS SYMBOLS The symbols of propositional calculus are the propositional symbols: P, Q, R, S, '" truth symbols:true, false and connectives: /\, V, -', ~, = Propositional symbols denote propositions, or statements about the world that may be either true or false, such as "the car is red" or "water is wet." Propositions are denoted by uppercase letters near the end of the English alphabet Sentences in the propositional calculus are fanned from these atomic symbols according to the following rules: PROPOSITIONAL CALCULUS SENTENCES Every propositional symbol and truth symbol is a sentence. For example: true, P, Q, and R are sentences. The negation of a sentence is a sentence. For example: --, P and -, false are sentences. The conjunction, or and, of two sentences is a sentence. For example: P /\ -, P is a sentence. The disjunction, or or, of two sentences is a sentence. For example: P v --, P is a sentence. The implication of one sentence from another is a sentence. For example: P -7 Q is a sentence. 11

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The equivalence of two sentences is a sentence. For example: P v Q = R is a sentence. Legal sentences are also called well-formed formulas or WFFs. In expressions of the form P A Q, P and Q are called the conjuncts. In P v Q, P and Q are referred to as disjuncts. In an implication, P -7 Q, P is the premise or antecedent and Q, the conclusion or consequent. In propositional calculus sentences, the symbols ( ) and [ ] are used to group symbols nto sub expressions and so to control their order of evaluation and meaning. The Semantics of the Propositional Calculus In this section we formally define the semantics or "meaning" of these sentences. Because AI programs must reason with their representational structures, it is important to demonstrate that the truth of their conclusions depends only on the truth of their initial knowledge, i.e., that logical errors are not introduced by the inference procedures. A precise treatment of semantics is essential to this goal. A proposition symbol corresponds to a statement about the world. For example, P may 'denote the statement "it is raining" or Q, the statement "I live in a brown house." A proposition may be either true or false, given some state of the world. The truth value assignment to propositional sentences is called an interpretation, an assertion about their truth in some possible world. Formally, an interpretation is a mapping from the propositional symbols into the set T. F}.

PROPOSITIONAL CALCULUS SEMANTICS An interpretation of a set of propositions is the assignment of a truth value, either T or F, to each propositional symbol. The symbol true is always assigned T, and the symbol false is assigned F.

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Predicate Calculus In propositional calculus, each atomic symbol (P, 0, etc.) denotes a proposition of some complexity. There is no way to access the components of an individual assertion. Predicate calculus provides this ability. For example, instead of letting a single propositional symbol, P, denote the entire sentence "it rained on Tuesday," we can create a predicate weather that describes a relationship between a date and the weather: weather (tuesday, rain). Through inference rules we can manipulate predicate calculus expressions, accessing their individual components and inferring new sentences. Predicate calculus also allows expressions to contain variables. Variables let us create general assertions about classes of entities. For example, we could state that for all values of X, where Xis a day of the week, the statement weather(X, rain) is true; i.e., it rains every day. As with propositional calculus, we will first define the syntax of the language and then discuss its semantics. The Syntax of Predicates and Sentences Before defining the syntax of correct expressions in the predicate calculus, we define an alphabet and grammar for creating the symbols of the language. This corresponds to the lexical aspect of a programming language definition. Predicate calculus symbols, like the tokens in a programming language, are irreducible syntactic elements: they cannot be broken into their component parts by the operations of the language. In this text we represent predicate calculus symbols as strings of letters and digits beginning with a letter. Blanks and non alphanumeric characters cannot appear within the string, although the underscore, _, may be used to improve readability. PREDICATE CALCULUS SYMBOLS The alphabet that makes up the symbols of the predicate calculus consists of: 1. The set of letters, both upper- and lowercase, of the English alphabet. 2. The set of digits, 0, 1, ... ,9.

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3. The underscore, _. Symbols in the predicate calculus begin with a letter and are followed by any sequence of these legal characters. Legitimate characters in the alphabet of predicate calculus symbols include aR69p_z Examples of characters not in the alphabet include #%@/&""

Parentheses '"( )", commas ':", and periods "." are used solely to construct well-formed expressions and do not denote objects or relations in the world. These are called improper symbols. Predicate calculus symbols may represent either variables, constants, functions, or predicates. Constants name specific objects or properties in the world. Constant symbols must begin with a lowercase letter. Predicate calculus also allows functions on objects in the world of discourse. Function symbols (like constants) begin with a lowercase letter. Functions denote a mapping of one or more elements in a set (called the domain of the function) into a unique element of another set (the range of the function). Elements of the domain and range are objects in k world of discourse. In addition to common arithmetic functions such as addition and multiplication, functions may define mappings between nonnumeric domains. SYMBOLS and TERMS Predicate calculus symbols include: 1. Truth symbols true and false (these are reserved symbols). 2. Constant symbols are symbol expressions having the first character lowercase. 3. Variable symbols are symbol expressions beginning with an uppercase character. 4. Function symbols are symbol expressions having the first character lowercase.

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Functions have an attached arity indicating the number of elements of the domain mapped onto each element of the range. A function expression consists of a function constant of atiry n, followed by n terms, t1, t2 , ……, tn, enclosed in parentheses and separated by commas. A predicate calculus term is either a constant, variable, or function expression. Thus, a predicate calculus term may be used to denote objects and properties in a problem domain. Examples of terms are: PREDICATES and ATOMIC SENTENCES Predicate symbols are symbols beginning with a lowercase letter. Predicates have an associated positive integer referred to as the arity or "argument number" for the predicate. Predicates with the same name but different arities are considered distinct. An atomic sentence is a predicate constant of arity n, followed by n terms, t., 1" ..., t, enclosed in parentheses and separated by commas. The truth values, true and false, are also atomic sentences. Atomic sentences are also called atomic expressions, atoms, or propositions. We may combine atomic sentences using logical operators to form sentences in the predicate calculus. These are the same logical connectives used in propositional calculus: A, v, -', ---7, and =. When a variable appears as an argument in a sentence, it refers to unspecified objects in the domain. First order (Section 2.2.2) predicate calculus includes two symbols, the variable quantifiers V and 3, that constrain the meaning of a sentence containing a variable. A quantifier is followed by a variable and a sentence, such as 3 Y friends(Y, peter) V X likes(X, ice_cream)

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The universal quantifier, v, indicates that the sentence is true for all values of the variable. In the example, V X likes(X, ice_cream) is true for all values in the domain of the definition of X. The existential quantifier, 3, indicates that the sentence is true for at least one value in the domain. 3 Y friends(Y, peter) is true if there is at least one object, indicated by Y that is a friend of peter. Sentences in the predicate calculus are defined inductivefy. PREDICATE CALCULUS SENTENCES Every atomic sentence is a sentence. 1. If 5 is a sentence, then so is its negation, ---, 5. 2. If s, and 52 are sentences, then so is their conjunction, 5 j 1\ 52' 3. If 81 and 82 are sentences, then so is their disjunction, s, v 52' 4. If 51 and Sz are sentences, then so is their implication, 51 ~ 82,

LISP Overview •

•

Lisp stands for “LISt Process” –

Invented by John McCarthy (1958)

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Simple data structure (atoms and lists)

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Heavy use of recursion

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Prefix notation of expressions

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Interpretive language

Why Lisp –

It is the most widely used AI programming language

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It is good for writing production software

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It is especially good for prototyping

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It has got lots of features other languages don’t

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You can write new programs and extend old programs really, really quickly in Lisp

Table of Contents • • • • • • • • • • • • • • • • • • • • • • • •

Symbols Numbers Conses Lists Functions Printing Forms and the Top-level Loop Special Forms Binding Dynamic Scoping Arrays Strings Structures Setf Booleans and Conditionals Iteration Non-local Exits Funcall, Apply, and Mapcar Lambda Sorting Equality Some Useful List Functions Getting Started with Emacs Further Information

Symbols

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A symbol is just a string of characters. There are restrictions on what you can include in a symbol and what the first character can be, but as long as you stick to letters, digits, and hyphens, you'll be safe. (Except that if you use only digits and possibly an initial hyphen, LISP will think you typed an integer rather than a symbol.) Some examples of symbols: a b c1 foo bar baaz-quux-garply

Some things you can do with symbols follow. (Things in bold after a > prompt are what you type to the LISP interpreter, while other things are what the LISP interpreter prints back to you. The ; is LISP's comment character: everything from a ; to the end of line is ignored.)

> (setq a 5) ;store a number as the value of a symbol 5 > a ;take the value of a symbol 5 > (let ((a 6)) a) ;bind the value of a symbol temporarily to 6 6 > a ;the value returns to 5 once the let is finished 5 > (+ a 6) ;use the value of a symbol as an argument to a function 11 > b ;try to take the value of a symbol which has no value Error: Attempt to take the value of the unbound symbol B

t

and nil

There are two special symbols, t and nil. The value of t is defined always to be t, and the value of nil is defined always to be nil. LISP uses t and nil to represent true and false. An example of this use is in the if statement, described more fully later: > (if t 5 6) 5 > (if nil 5 6) 6 > (if 4 5 6) 5

keyword The last example is odd but correct: nil means false, and anything else means true. (Unless we have a reason to do otherwise, we use t to mean true, just for the sake of 18

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clarity.) Symbols like t and nil are called self-evaluating symbols, because they evaluate to themselves. There is a whole class of self-evaluating symbols called keywords; any symbol whose name starts with a colon is a keyword. (See below for some uses for keywords.) Some examples: > :this-is-a-keyword :THIS-IS-A-KEYWORD > :so-is-this :SO-IS-THIS > :me-too :ME-TOO

Numbers An integer is a string of digits optionally preceded by + or -. A real number looks like an integer, except that it has a decimal point and optionally can be written in scientific notation. A rational looks like two integers with a / between them. LISP supports complex numbers, which are written #c(r i) (where r is the real part and i is the imaginary part). A number is any of the above. Here are some numbers: 5 17 -34 +6 3.1415 1.722e-15 #c(1.722e-15 0.75)

The standard arithmetic functions are all available: +, -, *, /, floor, ceiling, mod, sin, cos, tan, sqrt, exp, expt, and so forth. All of them accept any kind of number as an argument. +, -, *, and / return a number according to type contagion: an integer plus a rational is a rational, a rational plus a real is a real, and a real plus a complex is a complex. Here are some examples: > (+ 3 3/4) 15/4 > (exp 1) 2.7182817 > (exp 3) 20.085537 > (expt 3 4.2) 100.90418 > (+ 5 6 7 (* 8 9 10)) 738

;type contagion ;e ;e*e*e

;exponent with a base other than e ;the fns +-*/ all accept multiple arguments

There is no limit to the absolute value of an integer except the memory size of your computer. Be warned that computations with bignums (as large integers are called) can be slow. (So can computations with rationals, especially compared to the corresponding computations with small integers or floats.)

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Subfields of AI The subfields of artificial intelligence can be classified in terms of their role in either perception, reasoning, or actuation. • Perception – computer vision – natural language processing • Reasoning (i.e., problem solving): mapping from percepts to actuators – automated reasoning – knowledge representation – search and optimization – decision/game theory – machine learning • Actuation – robotics –

softbotics

Intelligent Agents Intelligent Agents The primary goal of (weak) artificial intelligence is to build intelligent entities. A related (but not a necessary) goal is to understand intelligent entities, and perhaps even to understand and engineer human intelligence (strong AI). Modern AI can be characterized as the engineering of rational agents. An agent is an entity that (i) perceives, (ii) reasons, and (iii) acts. In computational terms, that which is perceived is an input; to reason is to compute; to act is to output the result of computation. Typically, an agent is equipped with objectives. A rational agent is one that acts optimally with respect to its objectives. Agents are often distinguished from typical

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computational processes by their autonomy—they operate without direct human intervention. In addition, agents are reactive—they perceive their environments, and attempt to respond in a timely manner to changing conditions—and proactive—their behavior is goal directed, rather than simply response-driven. Autonomous agents may be rule-based, goal-based, or utility-based. Rule-based agents operate according to hard-coded sets of rules, like ELIZA. A goal-based agent acts so as to achieve its goals, by planning a path from its current state to a goal state, like GPS or theorem provers. Utility-based agents distinguish between goals, based on utilities that are associated with goal states.

Agent

Sensors

Actuators

Human

Senses

Arms, Legs

Robotic

Cameras

Motors, Wheels

Software

Bit Strings

Bit Strings

Figure Intelligent Agent 21

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Agent environments may be at least partially characterized as follows: • Deterministic vs. Nondeterministic: is the next state predictable (e.g., chess), or is there uncertainty about state transitions (eg, backgammon)? • Discrete vs. Continuous: can the environment be described in discrete terms (e.g., chess), or is the environment continuous (e.g., driving)? • Static vs. Dynamic: is the environment static (e.g., chess), or can it change while the agent is reasoning about its plan of action (e.g., driving)? • Sequential vs. One-shot: does the agent need to reason about the future impact of its immediate actions (e.g., chess), or can it treat each action independently (e.g., Rochambeau)? • Single agent vs. Multiagent: can we assume the agent operating alone in its environment, or need it explicitly reason about the actions of other agents (e.g., chess, backgammon, Rochambeau, driving)?

An agent is a system that perceives its environment through sensors and acts upon that environment through effectors.

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An agent can be seen as a mapping between percept sequences and actions. Agent : Percept → Action The less an agents relies on its built-in knowledge, as opposed to the current percept sequence, the more autonomous it is. A rational agent is an agent whose acts try to maximize some performance measure. Example: Vacuum-cleaner Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp

function REFLEX-VACUUM-AGENT( [location,status]) returns

action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left Intelligent Agents • • • •

Agent: entity in a program or environment capable of generating action. An agent uses perception of the environment to make decisions about actions to take. The perception capability is usually called a sensor. The actions can depend on the most recent perception or on the entire history (percept sequence).

Agent Function • • • •

The agent function is a mathematical function that maps a sequence of perceptions into action. The function is implemented as the agent program. The part of the agent taking an action is called an actuator. environment -> sensors -> agent function -> actuators -> environment

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Agent Classification: (a)Table-driven agents –

use a percept sequence/action table in memory to find the next action. They are implemented by a (large) lookup table.

(b) Simple reflex agents –

are based on condition-action rules, implemented with an appropriate production system. They are stateless devices which do not have memory of past world states.

(c) Agents with memory –

have internal state, which is used to keep track of past states of the world.

(d) Agents with goals –

are agents that, in addition to state information, have goal information that describes desirable situations. Agents of this kind take future events into consideration

(e) Utility-based agents

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base their decisions on classic axiomatic utility theory in order to act rationally.

Rational Agent • • •

A rational agent is one that can take the right decision in every situation. Performance measure: a set of criteria/test bed for the success of the agent's behavior. The performance measures should be based on the desired effect of the agent on the environment.

Rationality • • • • • •

The agent's rational behavior depends on: the performance measure that defines success the agent's knowledge of the environment the action that it is capable of performing the current sequence of perceptions. Definition: for every possible percept sequence, the agent is expected to take an action that will maximize its performance measure.

Agent Autonomy • • • •

An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice. An environment can sometimes be completely known in advance. Exploration: sometimes an agent must perform an action to gather information (to increase perception). Autonomy: the capacity to compensate for partial or incorrect prior knowledge (usually by learning).

Environment • • • • • •

Task environment - the problem that the agent is a solution to. Properties: Observable - fully or partially A fully observable environment needs less representation. Deterministic or stochastic Strategic -deterministic except for the actions of other agents.

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Environment • • • • • • •

Episodic or sequential Sequential - future actions depend on the previous ones. Episodic - individual unrelated tasks for the agent to solve. Static - dynamic Discrete - continuous Single agent - multi agent Multiple agents can be competitive or cooperative.

More Definitions of Agents • •

•

"An agent is a persistent software entity dedicated to a specific purpose. " (Smith, Cypher, and Spohrer 94 ) "Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires." (IBM) "Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. "(Hayes-Roth 94)

Agent vs. Program • • • •

Size - an agent is usually smaller than a program. Purpose - an agent has a specific purpose while programs are multi-functional. Persistence - an agent's life span is not entirely dependent on a user launching and quitting it. Autonomy - an agent doesn't need the user's input to function.

Simple Agents • • • • • •

Table-driven agents: the function consists in a lookup table of actions to be taken for every possible state of the environment. If the environment has n variables, each with t possible states, then the table size is tn. Only works for a small number of possible states for the environment. Simple reflex agents: deciding on the action to take based only on the current perception and not on the history of perceptions. Based on the condition-action rule: (if (condition) action) Works if the environment is fully observable

; Table driven, Lisp version (defun table_agent (percept)

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(let ((action t)) (push percept percepts) (setq action (lookup percepts table)) action)) (defun reflex_agent (percept) (let ((rule t) (state t) (action t)) (setq state (interpret percept)) (setq rule (match state)) (setq action (decision rule)) action)) # Table drives, Python version percepts = [] table = {} def table_agent (percept): action = True percepts.append(percept) action = lookup(percepts, table) return action def reflex_agent (percept): state = interpret(percept) rule = match(state) action = decision(rule) return action Model-Based Reflex Agents • • •

If the world is not fully observable, the agent must remember observations about the parts of the environment it cannot currently observe. This usually requires an internal representation of the world (or internal state). Since this representation is a model of the world, we call this model-based agent.

; Reflex agents, Lisp version (setq state t) ; the world model (setq action nil) ; latest action (defun model_reflex_agent (percept) (let ((rule t)) (setq state (update_state state action percept)) (setq rule (match state)) (setq action (decision rule)) action))

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# Reflex agents, Python version state = True # the world model action = False # latest action def model_reflex_agent (percept) state = update_state(state, action, percept) rule = match(state) action = decision(rule) return action Goal-Driven Agents • • • • • • • •

The agent has a purpose and the action to be taken depends on the current state and on what it tries to accomplish (the goal). In some cases the goal is easy to achieve. In others it involves planning, sifting through a search space for possible solutions, developing a strategy. Utility-based agents: the agent is aware of a utility function that estimates how close the current state is to the agent's goal. Choose actions so as to achieve a (given or computed) goal. A goal is a description of a desirable situation. Keeping track of the current state is often not enough − need to add goals to decide which situations are good Deliberative instead of reactive. May have to consider long sequences of possible actions before deciding if goal is achieved – involves consideration of the future, “what will happen if I do...?”

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Artificial Intelligence

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Learning Agents • • • • • •

Agents capable of acquiring new competence through observations and actions. Components: learning element (modifies the performance element) performance element (selects actions) feedback element (critic) exploration element (problem generator).

Table-driven agents • •

Table lookup of percept-action pairs mapping from every possible perceived state to the optimal action for that state Problems – Too big to generate and to store (Chess has about 10120 states, for example) – No knowledge of non-perceptual parts of the current state – Not adaptive to changes in the environment; requires entire table to be updated if changes occur – Looping: Can’t make actions conditional on previous actions/states

Simple reflex agents 29

Artificial Intelligence • •

Chapter1 Anurag Dixit

Rule-based reasoning to map from percepts to optimal action; each rule handles a collection of perceived states Problems – Still usually too big to generate and to store – Still no knowledge of non-perceptual parts of state – Still not adaptive to changes in the environment; requires collection of rules to be updated if changes occur – Still can’t make actions conditional on previous state

Utility-based agents • • • •

When there are multiple possible alternatives, how to decide which one is best? A goal specifies a crude distinction between a happy and unhappy state, but often need a more general performance measure that describes “degree of happiness.” Utility function U: State → Reals indicating a measure of success or happiness when at a given state. Allows decisions comparing choice between conflicting goals, and choice between likelihood of success and importance of goal (if achievement is uncertain).

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Artificial Intelligence

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Agents with memory • •

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Encode “internal state” of the world to remember the past as contained in earlier percepts. Needed because sensors do not usually give the entire state of the world at each input, so perception of the environment is captured over time. “State” is used to encode different "world states" that generate the same immediate percept. Requires ability to represent change in the world; one possibility is to represent just the latest state, but then can’t reason about hypothetical courses of action.

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Other Types of Agents • • • • •

Temporarily continuous - a continuously running process, Communicative agent - exchanging information with other agents to complete its task. Mobile agent - capable of moving from one machine to another one (or from one environment to another). Flexible agent - whose actions are not scripted. Character - an agent with conversation skills, personality, and even emotional state

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Artificial Intelligence

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