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AI Advancements: A Comprehensive Overview Since Quals

Discover the evolution of AI post-Quals, from machine learning to intelligent agents, user interfaces, applications in games like chess and backgammon, and AI on the web. Explore topics on overfitting, ensembles, softbots, and more. Uncover the cutting-edge research at UW, including planning, machine learning, and intelligent web applications. Learn about startups born out of UW/AI innovations.

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AI Advancements: A Comprehensive Overview 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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