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What Happened in AI Since Quals?

What Happened in AI Since Quals?. Corin Anderson (corin@cs) Steve Wolfman (wolf@cs) Tessa Lau (tlau@cs). Applications. Games Chess: brute force search Backgammon: reinforcement learning Bridge: HTN, Monte Carlo simulation Crosswords: combination of many expert modules

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What Happened in AI Since Quals?

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  1. What Happened in AI Since Quals? Corin Anderson (corin@cs) Steve Wolfman (wolf@cs) Tessa Lau (tlau@cs)

  2. Applications • Games • Chess: brute force search • Backgammon: reinforcement learning • Bridge: HTN, Monte Carlo simulation • Crosswords: combination of many expert modules • Deep Space One: Modeling, SAT-like planning • Automatic grading: Latent Semantic Indexing • RoboCup

  3. p nop p nop p a a s nop s q nop q nop q r nop r nop r Planning • The last thing you remember: UCPOP • Graphplan • SATPLAN • Encode planning problem in Boolean Satisfiability (proposition logic) • Solve logic problem with general-purpose algorithms

  4. 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

  5. Machine Learning: 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

  6. Intelligent Agents • Softbots • Combine traditional AI with new domains/techniques • Directions • Multiple agents and cooperation • Economic models: auctions • Learning about other agents • Learning about the environment • Human-agent interaction

  7. Intelligent User Interfaces • Programming by demonstration (PBD) • System learns program by watching user perform task • Bayesian networks • What’s the probability that the user wants to perform task X? Ex: MS Office Help facility

  8. Text, Images • Text • Latent Semantic Indexing • Cross-language corpora • WordNet • Images • Segmentation • Face recognition • Sign language recognition

  9. AI and the Web A rich environment for applications • Planning for information retrieval • Data extraction • Wrappers • Shopping on the web • Finding product price, description, etc. • Information agents • Collaborative filtering; sorting news; etc. • Data mining • Text understanding

  10. AI at the UW • Planning • SGP - Graphplan-based planner • LPSAT - SATPLAN-based planner • Machine learning • RISE - Occam’s razor isn’t always sound advice

  11. More AI at the UW • Web work • Adaptive web sites • Metacrawler, HuskySearch • Jango • Intelligent agents • PBD - learning macros

  12. Startups from UW/AI • NetBot (Weld, Etzioni) • Internet shopping agent (Jango project) • Purchased by Excite • Nimble.com (Weld, Levy) • XML data management (mumble, mumble) • Ad Relevance (Weld) • Target web advertising

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