Artificial Intelligence

What is AI?

• Can machines “think”?

• Can machines be truly autonomous?

• Can machines program themselves?

• Can machines learn?

• Will they ever be “conscious”, and is that necessary?

• media depictions of AI (science fiction)– HAL in 2001: Space Odessey– Spielberg’s AI– Data on Star Trek Next Generation– ...

• real AI has many practical applications– credit evaluation, medical diagnosis – guidance systems, surveillance – manufacturing (robotics, logistics)– information kiosks, computer-aided tutoring– AI in video games (also: Deep Blue, chess)– driverless vehicles, UAVs– Mars rover, Hubble telescope

• AI has a long history, and draws on many fields– mathematics, computability, formal logic– control theory– optimization– cognitive science– linguistics

Perspectives on AI

• Philosophical– What is the nature of intelligence?

• Psychological– How do humans think?

• Engineering– advanced methods for building complex

systems that solve hard real-world problems

Philosophical Perspective

• started with Greek philosophers (e.g. Aristotle)– syllogisms– natural categories

• 1700-1800s: development of logic, calculus– Descartes, Liebnitz, Boole, Frege, Tarski, Russell– what are concepts? existence, intention, causality...– reductionist approaches to try to mechanize reasoning

• 1900s: development of computers– input/output model– is intelligence a “computable function”?– Turing, von Neumann, Gödel

• Does “intelligence” require a physical brain?– Programmed devices will probably never have

“free will”

• Or is it sufficient to produce intelligent behavior, regardless of how it works?

• The Turing Test– first published in 1950– a panel of human judges asks questions through

a teletype interface (restricted to topic areas)– a program is intelligent if it can fool the judges

and look indistinguishable from other humans– annual competition at MIT: the Loebner Prize

• chatterbots

Psychological Perspective

• AI is about understanding and modeling human intelligence

• Cognitive Science movement (ca. 1950s)– replace stimulus/response model– internal representations– mind viewed as “information processor”

(sensory perceptionsconceptsactions)

• Are humans a good model of intelligence?– strengths:

• interpretation, dealing with ambiguity, nuance• judgement (even for ill-defined situations)• insight, creativity• adaptiveness

– weaknesses:• slow• error-prone• limited memory• subject to biases • influenced by emotions

Optimization• AI draws upon (and extends) optimization

– remember NP-hard problems?• there is (probably) no efficient algorithm that solves them in

polynomial time– but we can have approximation algorithms

• run in polynomial time, but don’t guarantee optimal solution– classic techniques: linear programming, gradient descent

• Many problems in AI are NP-hard (or worse)• AI gives us techniques for solving them

– heuristic search– use of expertise encoded in knowledge bases– AI relies heavily on greedy algorithms, e.g. for scheduling– custom algorithms for search (e.g. constraint

satisfaction), planning (e.g. POP, GraphPlan), learning (e.g. rule generation), decision making (MDPs)

Planning• Autonomy – we want computers to figure out

how to achieve goals on their own– Given a goal G

– and a library of possible actions Ak

– find a sequence of actions A1..An

– that changes the state of the worlds to achieve G

current state of world desired state of world

pickup(A)

puton(A,table)

pickup(C)

puton(C,A)

pickup(B)

pickup(B,C)

• Examples:– Blocks World – stack blocks in a desired way– traveling from College Station to Statue of Liberty– rescuing a victim in a collapsed building– cooking a meal

• The challenges of planning are:– for each task, must invoke sub-tasks to ensure pre-

conditions are satisfied• in order to nail 2 pieces of wood together, I have to have a

hammer

– sub-tasks might interact with each other• if I am holding a hammer and nail, I can’t hold the boards

– so planning is a combinatorial problem

Intelligent Agents• agents are: 1) autonomous, 2) situated in an

environment they can change, 3) goal-oriented

• agents focus on decision making

• incorporate sensing, reasoning, planning– sense-decide-act loop

• rational agents try to maximize a utility function (rewards, costs)goals KB initial state

goal state

perception

action

agent environment

• agents often interact in multi-agent systems– collaborative

• teamwork, task distribution• information sharing/integration• contract networks• voting• remember Dr. Shell’s multi-robots

– competitive• agents will maximize self utility in open systems• negotiation• auctions, bidding• game theory• design mechanisms where there is incentive to

cooperate

Core Concepts in AI• Symbolic Systems Hypothesis

– intelligence can be modeled as manipulating symbols representing discrete concepts

• like Boolean variables for CupEmpty, Raining, LightsOn, PowerLow, CheckmateInOneMove, PedestrianInPath...

• inference and decision-making comes from comparing symbols and producing new symbols

– Herbert Simon, Allan Newell (CMU, 1970s)

• (A competing idea: Connectionism)– neural networks– maybe knowledge can’t be represented by discrete

concepts, but is derived from associations and their strengths

– good model for perception and learning

Core Concepts in AI• Search

– everything in AI boils down to discrete search– search space: different possible actions

branch out from initial state– finding a goal

• weak methods: depth-first search (DFS), breadth-first search (BFS), constraint satisfaction (CSP)

• strong methods: use ‘heuristics’, A* searchS0

goal nodes

• Applications of search– game search (tic-tac-toe, chess)– planning– natural language parsing– learning (search for logical rules that describe

all the positive examples and no negative examples by adding/subtracting antecedents)

• Knowledge-representation– attempt to capture expertise of human experts– build knowledge-based systems, more powerful

than just algorithms and code– “In the knowledge lies the power” (Ed

Feigenbaum, Turing Award: 1994 )– first-order logic

p vegetarian(p)↔(f eats(p,f)m meat(m)contains(f,m)) x,y eat(joe,x)contains(x,y)fruit(y)vegetable(y) vegetarian(joe)

– inference algorithms• satisfiability, entailment, modus ponens, backward-

chaining, unification, resolution

Core Concepts in AI

• Expert Systems– Medical diagnosis: rules for linking symptoms

with diseases, from interviews with doctors– Financial analysis: rules for evaluating credit

score, solvency of company, equity-to-debt ratio, sales trends, barriers to entry

– Tutoring – rules for interpreting what a student did wrong on a problem and why, taxonomy of possible mis-conceptions

– Science – rules for interpreting chemical structures from mass-spectrometry data, rules for interpreting well logs and finding oil

• Major problem with many expert systems: brittleness

• Major issue in AI today: Uncertainty– using fuzzy logic for concepts like “good

management team”– statistics: conditional probability that a patient

has meningitis given they have a stiff neck– Markov Decision Problems: making decisions

based on probabilities and payoffs of possible outcomes

Sub-areas within AI• Natural language

– parsing sentences, representing meaning, metaphor, answering questions, translation, dialog systems

• Vision– cameraimagescorners/edges/surfaces

objectsstate description– occlusion, shading, textures, face recognition,

stereo(3D), motion(video)

• Robotics – configuration/motion planning• Machine Learning (machines can adapt!)

– decision trees, neural networks, linear classifiers– extract characteristic features from a set of examples