Hot Topics inArtificial Intelligence

Corin Anderson (corin@cs)

Tessa Lau (tlau@cs)

Steve Wolfman (wolf@cs)

Overview

• Applications• Planning• Machine Learning• Robots• Intelligent User Interfaces• The Web• Other stuff around here

Applications

• Games– Chess: brute force search– Backgammon: reinforcement learning– Bridge: HTN, Monte Carlo simulation– Crosswords: combination of many expert modules– Quake: situation-action rules

• Autonomous Spacecraft– Deep Space One: Modeling, SAT-like planning

Planning

• The last thing you remember: UCPOP– Least-commitment planning– Expressive, versatile, but slow

• Graphplan– “Mutex”

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More planning

• SATPLAN– Encode planning problem in Boolean Satisfiability

(proposition logic)– Solve logic problem with general-purpose algorithms

• UCPOP is back!– Apply Graphplan-like heuristics to UCPOP– It’s surprisingly fast!

Machine Learning

• Overfitting– Extensive search in hypothesis space causes

overfitting– Occam’s Razor is just one possible bias

• Scaling up to handle huge training sets– Make intermediate decisions with subsamples– Produce less accurate predictors with subsamples

and combine them into ensembles

ML: Ensembles

• Bagging– create k training sets by sampling real input set– Learn k predictors for the task, vote among them

• Boosting– Learn a predictor from weighted sample of real input– Change weights to emphasize misclassified points– Repeat– Vote resulting predictors according to accuracy

ML: Active Learning

• Traditionally:– “Teacher” provides fixed set of instances– Learner trains using all instances

• But this isn’t efficient– Instances may be redundant– Instances may be expensive to generate– Training time proportional to num. training instances

• Active learning– Learner queries for most-useful training instances

Robots

• Test environments– RoboCup flavors (small, medium, large, Aibo™)– Urban rescue

• Interesting issues– Localization: “R2D2, where are you?”– Autonomous map building– Cooperative robot teams

• Heterogeneous, homogeneous

Intelligent User Interfaces

• Programming by demonstration (PBD)– End-user programming for non-programmers– System learns program by watching user do task

• Bayesian networks– Graphical representation of variable dependence– Used for plan and goal recognition

• Mixed-initiative interfaces– AI does what AI is good at (fast brute force search)– Humans do what humans are good at (inspiration,

hunches, etc.)

AI and the Web

• A rich environment for applications– Information agents

• Collaborative filtering; sorting news; etc.

– Data mining– Text understanding

• An application on its own right– Web analysis (structure, usage)– Content personalization

AI at the UW (current and recent)

• Machine learning: VFDT– Very Fast Decision Tree– Learn a decision tree in “one pass” of data– Incremental computation for each datum is small– Application: large, streaming data sets

• Web logs• Cell phone calls• Credit card transactions

UW: Intelligent user interfaces

• SMARTedit – PBD system for text editing• SMARTpython – PBD system for learning

programs– Frame PBD as Machine Learning problem– Learn using very few training instances

• DIAManD – User interface for machine learning– General framework for learner/human interaction– When IUI meets active learning

UW: Web

• Adaptive web sites– Mine web logs for patterns of usage– Transform site to improve structure

• Index page synthesis

– Personalize content per visitor• Add, remove links• Highlight content• No irrevocable changes!• Emphasis towards wireless visitors

UW: Robotics

• Markov localization– Not really here, per se, but by Dieter Fox

• Sony AIBO RoboCup team (2001)• Just getting started…

Startups from UW/AI

• NetBot (Weld, Etzioni)– Internet shopping agent (Jango project)– Purchased by Excite

• Nimble.com (Weld, Halevy, et al.)– XML data management

• Ad Relevance (Weld)– Target web advertising