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

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applications
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
planning

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Planning
  • The last thing you remember: UCPOP
  • Graphplan
  • SATPLAN
    • Encode planning problem in Boolean Satisfiability (proposition logic)
    • Solve logic problem with general-purpose algorithms
machine learning
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
machine learning ensembles
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
intelligent agents
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
intelligent user interfaces
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
text images
Text, Images
  • Text
    • Latent Semantic Indexing
    • Cross-language corpora
    • WordNet
  • Images
    • Segmentation
    • Face recognition
    • Sign language recognition
ai and the web
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
ai at the uw
AI at the UW
  • Planning
    • SGP - Graphplan-based planner
    • LPSAT - SATPLAN-based planner
  • Machine learning
    • RISE - Occam’s razor isn’t always sound advice
more ai at the uw
More AI at the UW
  • Web work
    • Adaptive web sites
    • Metacrawler, HuskySearch
    • Jango
  • Intelligent agents
    • PBD - learning macros
startups from uw ai
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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