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Re Repr presentational Di Dimen ensions Com omputer Science c cpsc sc322, Lecture 2 2 (Te Text xtboo ook k Chpt1) Ma May, y, 1 16, 2 2017 CPSC 322, Lecture 2 Slide 1

Lectu ture re Ov Overv rvie iew • Recap ap fr from l las ast l lecture • Representation and Reasoning • An Overview of This Course • Further Dimensions of Representational Complexity CPSC 322, Lecture 2 Slide 2

Co Cours rse Ess ssenti tial als • Cou ourse se web-page ge : : CHECK IT OFTEN! • Te Text xtboo ook: Available online! • We will cover at least Chapters: 1, 3, 4, 5, 6, 8, 9 • Con onnect: discussion board, grades • AI AIsp space : online tools for learning Artificial Intelligence http://aispace.org/ • Lecture slides… • Midterm exa xam, , planning t g to o have in on on Wed J Jun 7 7 (will have a doo oodle on on piazz zza) CPSC 322, Lecture 2 Slide 3

Age gents ts ac acti ting g in in an an envi viro ronme ment Representation & Reasoning CPSC 322, Lecture 2 Slide 4

Lectu ture re Ov Overv rvie iew • Recap from last lecture • Repr presentat atio ion n an and d Reas asonin ing • An Overview of This Course • Further Dimensions of Representational Complexity CPSC 322, Lecture 2 Slide 5

What t do o we n need to to re repre rese sent t ? • Th The environ onment /wor orld : What different configurations (st states s / pos ossi sible wor orlds) can the world be in, and how do we denote them? Chessboard, Info about a patient, Robot Location • Ho How the w wor orld wor orks ks (we will focus on) • Con onst straints: s: sum of current into a node = 0 • Causa sal: what are the causes and the effects of brain disorders? • Ac Action ons preconditions and effects: when can I press this button? What happens if I press it? CPSC 322, Lecture 2 Slide 6

Cor orre resp spon ondin ing g Reaso sonin ing g Task sks s / P Pro roble lems • Con onst straint Satisf sfaction on – Fi Find st state that sa satisf sfies s se set of of con onst straints. s. E.g., What is a feasible schedule for final exams? • An Answ swering g Query – Is Is a gi given prop opos osition on true/like kely gi given what is s kn know own? E.g., Does this patient suffers from viral hepatitis? • Planning g – Fi Find se sequence of of action ons s to o reach a a go goal state / maxi st ximize ze utility. . E.g., Navigate through and environment to reach a particular location. Collect gems and avoid monsters CPSC 322, Lecture 2 Slide 7

Repre rese senta tati tion on an and Reas ason onin ing g Sy Syst stem • A (represe sentation on) langu guage ge in which the environment and how it works can be described • Computational (reaso soning) proc ocedures to compute a solution to a problem in that environment (an answer , a sequence of actions) But the choice of an appropriate R&R system Bu depends on a key property of the environment and of the agent’s knowledge CPSC 322, Lecture 2 Slide 8

Det eter ermi mini nist stic ic vs vs. Sto Stoch chas asti tic c (U (Unc ncer erta tain in) Dom omai ains • Se Sensi sing g Un Uncertainty: Can the agent fully observe the current state of the world? • Effect Un Uncertainty: Does the agent knows for sure what the effects of its actions are? Doctor Diagnosis Poker Factory Floor Chess Doctor T reatment CPSC 322, Lecture 2 Slide 9

Clic Cl icke ker r Qu Quest stio ion: Ch Chess ss an and Pok oker Sto tochas asti tic if at at l leas ast o t one of th these is tr true • Se Sensing Uncerta tainty ty: Can the agent fully observe the current state of the world? • Effect U t Uncerta tainty ty: Does the agent knows for sure what the effects of its actions are? 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 CPSC 322, Lecture 2 Slide 10

Dete term rmin inis isti tic vs vs. Sto Stochas asti tic Dom omai ains Historically, AI has been divided into two camps: those who prefer representations based on log ogic and those who prefer prob obability. A few years ago, CPSC 322 covered logic, while CPSC 422 422 introduced probability: • now we introduce both representational families in 322, and 422 goes into more depth • this should give you a better idea of what's included in AI No Note: Some of the most exciting current research in AI is actually building bridges between these camps. CPSC 322, Lecture 2 Slide 1 1

Lectu ture re Ov Overv rvie iew • Recap from last lecture • Representation and Reasoning • An Ov Overvi view of of T This is Co Course • Further Dimensions of Representational Complexity CPSC 322, Lecture 2 Slide 12

Mod odul ules es we' e'll ll co cove ver r in in th this is co cour urse se: R&R &Rsy sys Environ onment Stochastic Deterministic Prob oblem Arc Consistency Constraint Vars + Satisfaction Search Constraints Sta tati tic Belief Nets Logics Query Var. Elimination Search Decision Nets Se Sequenti tial al STRIPS Var. Elimination Planning Search Markov Processes Representation Value Iteration Reasoning CPSC 322, Lecture 2 Slide 13 Technique

Lectu ture re Ov Overv rvie iew • Recap from last lecture • Representation • An Overview of This Course • Fu Further Dim imensio ions of f Repr presentat atio iona nal l Compl Co plexit ity CPSC 322, Lecture 2 Slide 14

Dimensions of f Repr presentat ationa nal Co Compl plexity We've already disc scuss ssed: • Problems /Reasoning tasks (Static vs. Sequential ) • Deterministic versus stochastic domains Som ome ot other impor ortant dimensi sion ons s of of com omplexi xity: • Explicit state or propositions or relations • Flat or hierarchical • Knowledge given versus knowledge learned from experience • Goals versus complex preferences • Single-agent vs. multi-agent CPSC 322, Lecture 2 Slide 15

Exp xpli licit it Sta State te or or pro ropos osit itio ions How do we model the environment? • Y ou can enumerate the st states of the world. • A state can be described in terms of features • Often it is more natural to describe states in terms of assignments of values to features (variables). • 30 binary features (also called propositions) can represent 2 30 = 1,073,741,824 states. Mars s Exp xplor orer Exa xample Weather Temperature LocX LocY CPSC 322, Lecture 2 Slide 16

Exp xplicit t St State te or or pro ropos ositi tion ons s or or re relati tion ons • States can be described in terms of ob objects and relation onsh ships. • There is a proposition for each relationship on each “possible” tuple of individuals. Un Universi sity Exa xample Registred(S,C) Students (S) = { } Courses (C) = { } CPSC 322, Lecture 2 Slide 17

Cl Clic icke ker r Qu Quest stio ion One binary relation (e.g., likes ) and 9 individuals ( people ). How many states? A. 81 2 B. 10 2 C. 2 81 D. 10 9 I changed same-nationality to likes because if you reason on the meaning of same-nationality the states are less, they are 2 36 CPSC 322, Lecture 2 Slide 18

Co Comp mple lete te Exa xamp mple le CPSC 322, Lecture 2 Slide 19

Fl Flat at or or hie iera rarc rchic ical al Is it useful to model the whole world at the same level of abstraction? • Y ou can model the world at one level of abstraction: flat • Y ou can model the world at multiple levels of abstraction: hierarchical • Example: Planning a trip from here to a resort in Cancun, Mexico CPSC 322, Lecture 2 Slide 20

Knowle ledg dge giv iven vs vs. . knowle ledg dge le lear arned fr d from expe perie ience The agent is provided with a model of the world once and far all • The agent can learn how the world works based on experience • in this case, the agent often still does start out with some prior knowledge CPSC 322, Lecture 2 Slide 21

Goa oals ls ve vers rsus s (c (com omple lex) x) pre refe fere rences An agent may have a go goal that it wants to achieve • e.g., there is some sta tate te o or set o t of sta tate tes of the world that the agent wants to be in • e.g., there is some propositi tion on or set o t of propositi tion ons that the agent wants to make true An agent may have preferences • e.g., there is some preference/uti tility ty functi tion that describes how happy the agent is in each state of the world; the agent's task is to reach a state which makes it as happy as possible Preferences can be complex… What beverage to order? • The sooner I get one the better • Cappuccino better than Espresso CPSC 322, Lecture 2 Slide 22

Si Singl gle-ag agent t vs vs. Mult ltia iage gent t dom omai ains Does the environment include other agents? Everything we've said so far presumes that there is only one agent in the environment. • If there are other agents whose actions affect us, it can be useful to exp xplicitly mod odel their go goals s and b beliefs rather than considering them to be part of the environment • Other Agents can be: coo ooperative, com ompetitive, or a bit of of bot oth CPSC 322, Lecture 2 Slide 23