Artificial intelligence and Artificial intelligence and languagelanguage

Emmett TomaiUniversity of Texas – Pan American

What is Artificial What is Artificial Intelligence?Intelligence?Generally, the study of computation as

a model of human-like thought◦ The human mind is our best exemplar of

an artifact capable of intelligent thought Pattern recognition Logical reasoning Problem solving Planning Learning Language Social interaction Creativity

Goals and questionsGoals and questionsAI is a broad field, with many goals,

approaches, assumptions and unresolved questions◦ For some, the goal is to create an artificial mind◦ For others, to discover a formal model of thought◦ Others, to build machines (programs) that

perform difficult human-like tasks

Is computation sufficient to model human mental processes?

How do those processes differ from existing algorithms?

A bit of historyA bit of historyAI was established as a field in 1956 at

a research conferenceEarly work was largely symbolic and

deductive◦ Concepts represented as symbols in a

formal logical system◦ Reasoning carried out through step-by-step

deduction, attempting to mirror human processes

Logical problem solvingLogical problem solvingMany logical problems reduce to

search, trying to find an optimal path among many possibilities◦ Theorem proving◦ Constraint satisfaction (flight planning,

logistics problems)◦ Checkers, chess, go, etc.

However, complete logical search has serious problems with combinatoric explosion

The Traveling SalesmanThe Traveling SalesmanConsider a salesman who wants to

visit a bunch of small towns◦ He has a list of distances between pairs of

towns◦ How should he figure out what order to

visit them in?

Can be solved by straightforward search◦ How long do you think it would take you to figure

out the best path for 25 towns?

The Traveling SalesmanThe Traveling SalesmanConsider a salesman who wants to visit

a bunch of small towns◦ He has a list of distances between pairs of

towns◦ How should he figure out what order to visit

them in?

Finding the answer by straightforward search has complexity O(n!)◦ So it would take a 2GHz computer around a billion

years◦ Some more clever solutions are O(2n)

So how do people do it?So how do people do it?Unlike complete logical formalisms,

people solve problems quickly and easily using approximate methods◦ We don’t prove the best solution, we come

up with reasonable solutions (and quickly)◦ We use poorly understood mechanisms like

intuition, experience and creative thinking

Modern AI is largely concerned with numerous approaches to bridge that gap

SuccessesSuccessesMechanical self-diagnosis

◦ Used in autonomous spacecraftExpert systems

◦ Aide humans in identifying and solving known problems

Deep blue, etc.◦ Far better than the average human chess

player, can even beat the best humansGoogle

◦ Uses AI language processing techniques for search

Learn more about AILearn more about AIThe Association for the Advancement

of Artificial Intelligence (AAAI)◦ The biggest, general society for AI research

AI Topics at AAAI◦ Great resource◦ Seminal references for all major sub-fields

of AI◦ http://www.aaai.org/AITopics/pmwiki/pmwi

ki.php/AITopics/HomePage◦ (click on “Browse Topics”)

Language understanding Language understanding and AIand AIWhy would you want a computer to be

competent at natural language (e.g. English, Spanish, etc.)◦ Machine translation◦ Intelligent tutoring systems◦ Information retrieval (search engines)◦ Question answering systems◦ Information management (emails, blogs, news,

etc.)◦ Speech recognition◦ Storytelling tools (generation, editing,

evaluation, etc.)Bottom line: people use language to do a

lot of things

Language understanding Language understanding and AIand AIPeople have been studying language for a long

time◦ Literature, philosophy, linguistics◦ Anthropology, psychology

This has resulted in common levels of analysis◦ Morphology: how words are constructed◦ Lexicon: the words that are available in a language◦ Syntax: structural relationships between words◦ Semantics: the meaning or a word of combination of

words◦ Discourse: how meaning evolves over time in a

monologue or dialogue◦ Pragmatics: the purpose behind an utterance, how

language is used◦ World knowledge: facts about the world, common sense

Language understanding Language understanding and AIand AIEarly work (1950s) in machine

translation assumed that translation was a matter of lexicon (changing the words) and syntax (changing the order)

That proved far too simplistic:◦ hydraulic ram

= water sheep

◦ out of sight, out of mind = blind, crazy

◦ The spirit is willing but the flesh is weak. = The vodka is good but the meat is rotten.

Language understanding Language understanding and AIand AIChomsky (1957) provided a robust linguistic

theory that was specific enough to inform a computational model

Montague (1970) presented a formal, logical grammar casting syntax and semantics as a precise mathematical system

male( he )studies( he, linguistics )…

Language understanding Language understanding and AIand AINumerous problems remain, starting with

ambiguity◦ There is often more than one syntactically correct

parse:

◦ “Time flies like an arrow.” What’s the verb?

◦ “I saw the Grand Canyon flying to New York.” Who or what was flying?

◦ “I saw the man on the hill with the telescope.” Who was on the hill? Where was the telescope?

This creates the same problem of combinatorial explosion as the traveling salesman problem

Worse, real language is often syntactically incorrect

Language understanding Language understanding and AIand AISyntax alone isn’t enough to

understand language, you need semantics, pragmatics and world knowledge◦ How did you know the Grand Canyon

wasn’t the one flying?◦ Unfortunately, each of those bring in their

own ambiguities and difficulties

Language understanding Language understanding and AIand AILexical ambiguity

◦ I walked to the bank ... of the river. to get money.

◦ The bug in the room ... was planted by spies. flew out the window.

◦ I work for John Hancock ... and he is a good boss. which is a good company.

Language understanding Language understanding and AIand AICo-reference resolution

◦ President John F. Kennedy was assassinated.

◦ The president was shot yesterday.◦ Relatives said that John was a good father.◦ JFK was the youngest president in history.◦ His family will bury him tomorrow.◦ Friends of the Massachusetts native will

hold a candlelight service in Mr. Kennedy’s home town.

Language understanding Language understanding and AIand AIPragmatics

◦ Mostly studied in conversational dialogue, but applies to any linguistic communication

◦ Rules of Conversation Can you tell me what time it is?

4:30. Could I please have the salt?

<passes the salt> What platform does the 5:00 train leave from?

It already left.

◦ Speech Acts I bet you $50 that the Jazz will win.

You’re on. You’re fired!

Language understanding Language understanding and AIand AIWorld Knowledge

◦ John went to the diner. He ordered a steak. He left a tip and went home.

◦ John wanted to commit suicide. He got a rope.

Language understanding Language understanding and AIand AIA very hard problem, with a big

potential payoffAll the levels of analysis (lexical,

syntactic, etc.) must work together in understanding◦ But this leads to seemingly insurmountable

complexityMany approaches being pursued

◦ The same as for AI in general, trying to bridge the gap between explosive complexity in the formal system and the ease with which people do it every day

Knowledge is powerKnowledge is powerProblem: people know a lot of things

◦ Common sense reasoning (where the gap is huge) seems to involve using that knowledge

◦ Understanding the pragmatics of language requires being able to reason about the situation surrounding what is being said

Solutions?◦ Build huge knowledge bases filled with

common sense information that people might have

Experience is the best Experience is the best teacherteacherProblem: part of what people know is a

huge number of experiences◦ We remember prior events and apply them to

the current situation◦ We can even adapt similar but not identical

situations and ideas to understand new situations and ideas

Solutions?◦ Case-based reasoning, storing logical

representations of prior events◦ Analogical reasoning, being reminded of similar

things and appreciating how they compare and contrast

Learning is fundamentalLearning is fundamentalProblem: people learn as they go

◦ We continue to adapt and expand our knowledge and capabilities

◦ It’s not clear what we start with and what we learn

◦ We don’t do the same stupid thing twice

Solutions?◦ Perhaps this is the answer to the previous

two problems◦ Many researchers think that a legitimate AI

must involve learning, otherwise you’re just tweaking it to work on specific problems

Reactive approachesReactive approachesProblem: something as simple as an ant or

a fruit fly is capable of amazing navigation◦ Our models of intelligence require massive

computing power to simulate a fruit fly◦ Modern robots have trouble crossing the room

without crashing into thingsSolutions?

◦ Reactive architectures concentrate on doing simple operations really fast and really well Put enough of those together and maybe we’ll get

intelligence

◦ It may not scale up to writing poetry, but at least it can avoid running into walls

The statistical revolutionThe statistical revolutionProblem: formal, logical systems are

fragile and struggle with environments far less complex than reality◦ Can these really scale up to working in the

real world with ambiguity, vagueness and incomplete knowledge?

◦ With something as “inherent” as language, are we really reasoning about each sentence or are we relying on more subconscious, fast-acting mechanisms?

Solutions?◦ Most NL work in the last two decades has

focused on statistical methods

The statistical revolutionThe statistical revolutionStatistical methods use machine learning

algorithms to fit a model to the data◦ Given 1000s of training examples, a statistical

parser can achieve robust, high-performance results on similar text Effective on parsing, named entity extraction, noun-

noun co-reference resolution, semantic role labeling

◦ However, since it relies entirely on consistent patterns, a parser trained on one type of text (say the Wall Street Journal) performs poorly on another (say a textbook, novel or blog) Worse, this approach hasn’t been shown to scale very

far beyond simple syntactic features

Learning semanticsLearning semanticsContext constrains ambiguity

◦ Filters out possible meanings that don’t make sense

TRIPS (Allen et al)◦ The Rochester Interactive Planning System◦ Collaborative planning between human and AI◦ Shared goal provides context

Can reason about what the person might mean Enables impressive speech understanding

Learning requires constrained examples◦ Utterances…◦ …combined with an appropriate situation

(context)

Learning semantics for…?Learning semantics for…?Virtual spaces provide flexible

constraint◦ There are only so many moves in checkers◦ Only so many things you could try to do

Constraint enables planning, problem-solving◦ AI systems can reason about moves

Can that constraint enable learning language?

Learning semantics in Learning semantics in gamesgamesYou are playing checkers

◦ You already know how to play◦ Someone is giving you advice in Chinese◦ Could you learn some Chinese that way?

Learning semantics in Learning semantics in gamesgamesYou are playing checkers

◦ You already know how to play◦ Someone is giving you advice in Chinese◦ Could you learn some Chinese that way?

Assume that they know what they’re talking about◦ Hypothesize mappings

Words to items, actions in the game Phrases to reasonable moves they could be

suggesting

◦ Test hypotheses as the game goes on

Learning semantics in Learning semantics in gamesgamesYou are playing mahjongg

◦ You don’t know how to play◦ Someone is giving you advice in Chinese◦ Could you learn some Chinese, and

mahjongg at the same time?

Learning semantics in Learning semantics in gamesgamesYou are playing mahjongg

◦ You don’t know how to play◦ Someone is giving you advice in Chinese◦ Could you learn some Chinese, and mahjongg

at the same time?Assume that they know what they’re

talking about◦ Hypothesize mappings for words, phrases◦ Test hypotheses as the game goes on

Much harder since you can’t filter out bad/nonsensical moves

Have to play the game out to see what moves were good

Requires a lot of patience and methodical testing

Learning semantics in Learning semantics in gamesgamesInteractive learning would be faster

◦ Particularly with positive/negative feedbackKnowledge helps

◦ Syntax, lexical categories, other widely available knowledge

◦ The more you know about games in general, the fewer options you’ll have to try

Bootstrapping◦ Start with very simple goals to start

language learning◦ Build up to more complex goals

Planning in real-time Planning in real-time strategy gamesstrategy games

Lots of recent work on planning in RTS games◦ Not as neat and clean as classic games (chess, etc)◦ Require real-time decisions, uncertainty, heuristics◦ Lots of player and strategy analysis available◦ Still a limited environment compared to reality

Learning semantics in a RTS Learning semantics in a RTS gamegameGiven specific goals and varied instructions

to reach those goals, can an AI learn English semantics while learning to play an RTS game?

How can you tell if it does?◦ Evaluating language learning is hard and

subjective◦ Evaluating performance is easy

Do the instructions help the AI learn to play better, faster?

Once some language has been learned, can it go on to learn another game goal faster?

Project detailsProject detailsSpring – Summer 2011StarCraft: Brood War

◦ AI player using BroodWar API (BWAPI) C++ dll Event-driven programming Expose game info, unit commands Most likely bridge to a higher-level language (python,

lisp, java)

◦ Research existing dynamic planning agents◦ Implement a planning agent in-game◦ Test ability to plan and reach simple in-game

goalsFunded by a UTPA Faculty Research

Council grant for the summer RA position

Additional projectsAdditional projectsAdaptive narrative in shared, virtual worlds

◦ Many dynamic virtual world simulations Physics, economics, politics, etc.

◦ Narrative presentation is largely static Cut-scenes, quest text, dialogue trees, etc.

◦ How can we use AI techniques to create and present narrative that adapts to a dynamic, interactive environment?

Current project◦ Building up shared world infrastructure

Shared interactions in a persistent, physically-based world

◦ Using publically available tools, libraries, engines, etc.

Additional projectsAdditional projectsSimulating dramatic social interaction

◦ Physical interactions have been well simulated Physics-based movement and collision Combat abstractions

◦ Social interactions are less well explored Formal diplomacy (lacks emotions, personal

relationships) Bargaining (also abstract) Relationship models (Fable, The Sims)

Emergent story, independent of narrative/dramatic arcs

Curiosity-driven, lacking communicative goals