CS 730/830: Intro AI Class Outro AI at UNH Wheeler Ruml (UNH) - - PowerPoint PPT Presentation

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CS 730/830: Intro AI Class Outro AI at UNH Wheeler Ruml (UNH) - - PowerPoint PPT Presentation

CS 730/830: Intro AI Class Outro AI at UNH Wheeler Ruml (UNH) Lecture 27, CS 730 1 / 12 Class Outro The AI View Past Present Talk Paper Future Evaluations AI at UNH Class Outro Wheeler Ruml (UNH) Lecture 27,


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CS 730/830: Intro AI

Class Outro AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 1 / 12

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SLIDE 2

Class Outro

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 2 / 12

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SLIDE 3

The AI View of An Agent

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 3 / 12

percepts → → actions

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Past

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 4 / 12

perception: supervising learning (handwriting recognition), unsupervised learning (shape finding) [ HMMs ]

reasoning: constraint satisfaction, propositional satisfiability, first-order logic theorem proving [ tree search, optimization ]

planning: state-space search, motion planning, domain-independent task planning, planning under uncertainty (MDPs) [ anytime and real-time planning, reinforcement learning ]

acting: filtering (MCL) [ control ] Not: cognitive modeling, ethics, NLP, vision, philosophy of mind

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Present

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 5 / 12

Fri May 1: no recitation

Tue May 5 9-noon: project presentations 10+2 minutes/person

Mon May 11 2pm: final papers email PDF, tarball, HOWTO given tarball and HOWTO, raw results should be reproducible on agate

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Tips for A Research Talk

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 6 / 12

problem (example!), approach, results, extensions

practice beforehand: word choice, timing

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Tips for a Research Paper

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 7 / 12

use the standard form: introduction (motivate and define problem, summarize paper), previous work, your approach, experimental results, discussion, conclusion

write for someone who has taken an AI class but doesn’t know anything about your specific problem

don’t just plot results, explicitly describe what they show and the conclusions you draw from them

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Future

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 8 / 12

UNH AI group: usually weekly (Google ‘UNH AI group’) sign up for the mailing list! Fall:

( Wheeler Ruml: CS 931 Planning for Robots )

( Momotaz Begum: CS 733/833 Mobile Robotics )

( Laura Dietz: CS 753/853 Information Retrieval )

Laura Dietz: CS 780/880 ML for Sequences and Text

Marek Petrik: CS 950 Reinforcement Learning Spring:

Marek Petrik: CS 750/850 Machine Learning

Marek Petrik and Mark Lyon: CS 757/857 Optimization

Momotaz Begum: CS 780/880 Computer Vision

Momotaz Begum: CS 933 Human-Robot Interaction

Laura Dietz: CS 953 Knowledge Graphs and Text

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Evaluations

Class Outro ■ The AI View ■ Past ■ Present ■ Talk ■ Paper ■ Future ■ Evaluations AI at UNH

Wheeler Ruml (UNH) Lecture 27, CS 730 – 9 / 12

These are important! I take them seriously and so does my boss. For free response text, please address: 1. Things that were good about the class, things that need work. specific suggestions or general comments! 2. Things that I did well, things that I should work on. Things that Tianyi did well, things that Tianyi should work

  • n

Thanks.

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AI at UNH

Class Outro AI at UNH ■ AI at UNH ■ EOLQs

Wheeler Ruml (UNH) Lecture 27, CS 730 – 10 / 12

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AI at UNH

Class Outro AI at UNH ■ AI at UNH ■ EOLQs

Wheeler Ruml (UNH) Lecture 27, CS 730 – 11 / 12

Marek Petrik: robust RL

Momotaz Begum: assistive robotics

Laura Dietz: Queripedia

Wheeler Ruml: heuristic search, planning

rational real-time search (Tianyi)

suboptimal and bounded suboptimal (William)

real-time path coverage (Alex)

  • nline goal recognition design (Kevin)

motion planning in a dynamic environment (Yi)

group assignment (Brendan)

ROP attack assembly (Daroc)

physical TSP (Bryan, Lucas, Charles)

situated temporal planning (Shahaf)

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EOLQs

Class Outro AI at UNH ■ AI at UNH ■ EOLQs

Wheeler Ruml (UNH) Lecture 27, CS 730 – 12 / 12

Nope.