Computer Science CPSC 322 Le Lecture ture 2 Re Representational presentational Di Dimensions mensions 1
ANNOU NOUNC NCEMENT EMENT • You need to register your Clicker in Connect if you have never done so before • Otherwise your answers won’t be recorded • Assignment 0 due on Thurdsay • People on the wait list can find the assignment in Piazza (post @10) • You can send it to Vanessa via email by 4:30 on Th. if you want it to count, in case you get into the course 2
Te Teaching ching Te Team am Instr struc uctor tor • Cristina Conati ( conati@cs.ubc.ca; office ICICS/CS 107) Te Teachin hing As Assista istants nts • Borna Ghotbi (bghotbi@cs.ubc.ca) • Vanessa Putnam (vputnam@cs.ubc.ca) • Michael Przystupa (bot267@ugrad.cs.ubc.ca) • Wenyi Wang (wenyw@cs.ubc.ca) OFFICE HOURS NOW AVAILABLE ON THE WEBSITE
Today’s Lecture • Recap from last lecture • Representation and Reasoning: Dimensions • An Overview of This Course • Further Representational Dimensions • Intro to search (time permitting) 4
Course urse Essentials entials • Course website: CHECK IT OFTEN! htt ttp:// p://www.cs.ubc.ca w.cs.ubc.ca/~ /~con conat ati/3 i/322/32 22/322-20 2017W1/cou 17W1/course rse- page.ht .html ml • Syllabus, lecture slides, other material • Textbook: Artificial Intelligence: Foundations of Computational Agents. by Poole and Mackworth. (P&M) • Available electronically (free) http://artint.info/html/ArtInt.html • We will cover at least Chapters: 1, 3, 4, 5, 6, 8, 9 • Connect for assignments and marks • Piazza for Discussion board • AIspace pace : online tools for learning Artificial Intelligence http://aispace.org/ 5
Wh What t is is Ar Arti tificia ficial l In Intell telligence? igence? Clicker Question: We use the following definition • The study and design of A. Systems that think rationally B. Systems that act like humans C. Systems that act rationally D. Systems that think like humans 6
Wh What t is is Ar Arti tificia ficial l In Intell telligence? igence? Clicker Question: We use the following definition • The study and design of A. Systems that think rationally B. Systems that act like humans C. Systems that act rationally D. Systems that think like humans 7
AI a I as Stu tudy dy and d Design sign of f In Inte tell lligent igent Agents ents • Intelligent agents: artifacts that act rationally in their environment • Their actions are appropriate for their goals and circumstances • They are flexible to changing environments and goals • They learn from experience • They make appropriate choices given perceptual limitations and limited resources • This definition drops the constraint of cognitive plausibility • Same as building flying machines by understanding general principles of flying (aerodynamic) vs. by reproducing how birds fly
In Intel telligent ligent Agents ents in in th the Wo World ld Knowled ledge e Represen presentat tation ion Mach chine ine Lear arning ning abilities Reas asoning ning + Decis cision ion Theory ory Representatio esentation & Reason oning ng Natur ural al Languag nguage e Gener neration ion + + Natural ural Langua nguage e Robot botics ics Unde ders rstan anding ing + + + Compu mputer er Vis Visio ion Human man Computer mputer Speech ech Reco cogn gnitio ition /Robot bot + Inter eraction action Phys ysiologic iological al Sens nsing ing 9 Min inin ing g of Interaction eraction Logs gs
Today’s Lecture • Recap from last lecture • Representation and Reasoning: Dimensions • An Overview of This Course • Further Representational Dimensions • Intro to search (time permitting) 10
Representation presentation and d Reasoning asoning Representation & Reasoning To use these inputs an agent needs to represe resent nt them knowle wledge One of AI goals: specify how a system can • Acquire and represent knowledge about a domain (represe present ntation ation) • Use the knowledge to solve problems in that domain (reasoni asoning)
Rep eprese esenta ntati tion on an and R d Rea eason oning ng (R&R) &R) System stem • A representation language to describe • The environment • Problems (questions/tasks) to be solved • Computational reasoning procedures to compute a solution to a problem • E.g., an answer, sequence of actions problem ⟹ representation ⟹ computation ⟹ representation ⟹ solution • Choice of an appropriate R&R system depends on various dimensions, e.g. properties of • the environment, the type of problems, the agent, the 12 computational resources, etc.
Represent presentational ational Dim imensions ensions En Enviro ronm nment ent Stochastic Deterministic Pr Problem em Ty Type Each cell will We’ll start by describing include a R&R Static atic dimensions related to the system covered problem and environment in the course Sequential ntial Then we’ll include in each Then we’ll include in cell the various R&R each cell R&R system systems covered in the course, and discuss some covered in the course more dimensions
Probl oblem em Ty Types es • St Static: ic: finding a solution does not involve reasoning into the future (time is ignored) • One-step solution • Se Sequentia ntial: l: finding a solution requires looking for a number of steps into the future, e.g., • Fixed horizon (fixed number of steps) • Indefinite horizon (finite, but unknown number of steps) 14
Probl oblem em Ty Types es • Constraint Satisfaction – Find state that satisfies set of constraints (static). • e.g., what is a feasible schedule for final exams? • Answering Query – Is a given proposition true/likely given what is known? (static). • e.g., does the patient suffers from viral hepatitis? • Planning – Find sequence of actions to reach a goal state / maximize outcome (sequential). • e.g., Navigate through an environment to reach a particular location 15
Represent presentational ational Dim imensions ensions En Enviro ronm nment ent Stochastic Deterministic Problem Pr em Ty Type Constraint Satisfaction Static atic Query Sequential ntial Planning 16
Deterministic terministic vs vs. . St Stochastic chastic (Uncertain) ncertain) Environment ironment • Sensing ing Un Uncert rtai ainty ty: The agent cannot fully observe the current state of the world when acting Sensin ensing g Uncertai rtainty? nty? Teacher’s explanation Soccer Player Kick • Effect fect Un Uncerta rtain inty ty: : the agent does not know for sure the immediate effects of its actions 17
Deterministic terministic vs vs. . St Stochastic chastic (Uncertain) ncertain) Environment ironment • Se Sensin ing g Uncerta rtainty inty: The agent cannot fully observe the current state of the world Sen ensin sing g Uncert rtai ainty nty? Teacher’s explanation YES Soccer Player Kick NO • Ef Effec ect t Un Uncerta rtainty inty: : the agent does not know for sure the effects of its actions Effect ffect Uncertain rtainty? ty? Teacher’s explanation Soccer Player Kick 18
Deterministic terministic vs vs. . St Stochastic chastic (Uncertain) ncertain) Environment ironment • Se Sensin ing g Uncerta rtainty inty: The agent cannot fully observe the current state of the world Sen ensin sing g Uncert rtai ainty nty? Teacher’s explanation YES Soccer Player Kick NO • Effec Ef ect t Un Uncerta rtainty inty: : the agent does not know for sure the effects of its actions Effect ffect Uncertain rtainty? ty? Teacher’s explanation YES Soccer Player Kick YES 19
Cli licker ker Qu Question stion: : Chess ess and d Poker ker An environment is stochastic if at least one of these is true • Sensing ing Un Uncert rtai ainty ty: the agent cannot fully observe the current state of the world • Ef Effect fect Un Uncertainty: rtainty: the agent does not know for sure the immediate, direct effects of its actions A. Poker and Chess are both stochastic B. Chess is stochastic and Poker is deterministic C. Poker and Chess are both deterministic D. Chess is deterministic and Poker is stochastic 20
Cli licker ker Qu Question stion: : Chess ess and d Poker ker An environment is stochastic if at least one of these is true • Sensing ing Un Uncert rtai ainty ty: the agent cannot fully observe the current state of the world • Ef Effect fect Un Uncertainty: rtainty: the agent does not know for sure the immediate, direct effects of its actions A. Poker and Chess are both stochastic B. Chess is stochastic and Poker is deterministic C. Poker and Chess are both stochastic D. Chess is deterministic and Poker is stochastic 21
Determini terministic stic vs. . Sto tochastic chastic Domains mains • Historically, AI has been divided into two camps: those who prefer representations based on logic ic and those who prefer probab abil ilit ity. • In CPSC 322 we introduce both representational families, and 422 goes into more detail Note: Some of the most exciting current research in AI is actually building bridges between these camps. 22
Recommend
More recommend
