Course wrap up J uly 26, 2005 CS 486/ 686 Universit y of Wat - PowerPoint PPT Presentation

Course wrap up J uly 26, 2005 CS 486/ 686 Universit y of Wat erloo

Out line • Course wrap up • Final exam inf o • Ot her AI courses • AI j obs • AI research 2 CS486/686 Lecture Slides (c) 2005 P. Poupart

Agent s and Environment s sensors percept s environment ? agent act ions act uat ors Agent s include humans, robot s, sof t bot s, t hermost at s… The agent f unct ion maps percept s t o act ions f :P* � A The agent pr ogram runs on t he physical archit ect ure t o produce f 3 CS486/686 Lecture Slides (c) 2005 P. Poupart

Rat ional Agent s • Recall: A rat ional agent “does t he right t hing” • Perf ormance measure – success crit eria – Evaluat es a sequence of environment st at es • A r at ional agent chooses whichever act ion maximizes t he expect ed value of it s perf ormance measure given t he percept sequence t o dat e – Need t o know perf ormance measure, environment , possible act ions, percept sequence • Rat ionalit y ≠ Omniscience, Perf ect ion, Success • Rat ionalit y � explorat ion, learning, aut onomy 4 CS486/686 Lecture Slides (c) 2005 P. Poupart

Bounded Rat ionalit y • What if t he best st rat egy given past percept s cannot be implement ed wit h t oday’s comput ers? • We have seen many t heories f or rat ional agent s but what if t hose t heories are int ract able? • Bounded rat ionalit y: f ind best implement able st rat egy given past percept s 5 CS486/686 Lecture Slides (c) 2005 P. Poupart

Ot her AI courses • CS498: Machine Learning – St at ist ical and Comput at ional Foundat ions • CS498: I mage and vision comput ing • CS785: I nt elligent Comput er I nt eract ion • CS886: Topics in AI : Reasoning under Uncert aint y • CS886: Topics in AI : Knowledge represent at ion 6 CS486/686 Lecture Slides (c) 2005 P. Poupart

CS498: Machine Learning – St at ist ical and Comput at ional Foundat ions • I nst ruct or: Shai Ben David • Term: Wint er 2006 • Obj ect ives: – The course is aimed t o f amiliarize t he st udent s wit h t he basic t heoret ical t ools and issues underlying some of t he most usef ul machine learning t echniques. The t heory of machine learning draws f rom several est ablished mat hemat ical areas including st at ist ics, geomet ry, combinat orics, and comput at ional complexit y. 7 CS486/686 Lecture Slides (c) 2005 P. Poupart

CS785 I nt elligent Comput er I nt eract ion • I nst ruct or: Robin Cohen • Term: Fall 2005 or Spring 2006 • Topics: – mult iagent syst ems, – int elligent t ut oring syst ems and knowledge-based syst ems, – dat amining, – user modeling, – nat ural language generat ion and dialogue, – plan recognit ion 8 CS486/686 Lecture Slides (c) 2005 P. Poupart

CS886: Topics in AI : Reasoning under Uncert aint y • I nst ruct or: P ascal Poupart • Term: Fall 2005 • Obj ect ives: – This course will f ocus on t he principles of probabilist ic reasoning and sequent ial decision making f or a wide range of set t ings including adapt ive and mult i-agent syst ems. The modeling t echniques t hat will be covered are quit e versat ile and can be used t o t ackle a wide range of problems in many f ields including robot ics (e.g., mobile robot navigat ion, cont rol), comput er syst ems (e.g., aut onomic comput ing, query opt imizat ion), human-comput er int eract ion (e.g., spoken dialog syst ems, user modeling), bioinf ormat ics (e.g., gene sequencing, design of experiment s), oper at ions research (e.g., resource allocat ion, maint enance scheduling, planning), et c. Hence, t he course should be of int erest t o a wide audience beyond art if icial int elligence. 9 CS486/686 Lecture Slides (c) 2005 P. Poupart

CS886: Topics in AI : Knowledge Represent at ion • I nst ruct or: Chrysanne DiMarco • Term: Spring 2006 • Topics: TBA 10 CS486/686 Lecture Slides (c) 2005 P. Poupart

AI research group • Web: ai.uwat erloo.ca • Prof essors: – Shai Ben David (learning t heory) – Chrysanne DiMarco (nat ural language processing) – Pet er Van Beek (const raint programming) – Robin Cohen (mult i-agent syst ems, user modeling) – Pascal Poupart (reasoning under uncert aint y, machine learning) – Kat e Larson (game t heory, mechanism design) – Richard Mann (comput at ional vision) 11 CS486/686 Lecture Slides (c) 2005 P. Poupart

AI j obs • Very f ew “AI companies” • AI t ends t o be embedded in many applicat ions • Many companies have AI R&D groups – I nt el, Microsof t , I BM, Google, NEC, Yahoo, HP • AI is a growing indust r y • Has t he pot ent ial t o revolut ionize t he comput er indust ry! 12 CS486/686 Lecture Slides (c) 2005 P. Poupart