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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
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