CS343 Artificial Intelligence
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1 CS343 Artificial Intelligence Prof: Department of Computer Science The University of Texas at Austin
2 Good Afternoon, Colleagues
3 Good Afternoon, Colleagues Are there any questions?
4 Logistics Problems with the assignment?
5 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation
6 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list
7 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list CC Daniel and me on everything
8 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list CC Daniel and me on everything Use correct subject line
9 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list CC Daniel and me on everything Use correct subject line Next week s assignments up
10 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list CC Daniel and me on everything Use correct subject line Next week s assignments up Comments on Forum for AI talk last Friday?
11 Logistics Problems with the assignment? 3.1: Goal formulation vs. problem formulation Mailing list CC Daniel and me on everything Use correct subject line Next week s assignments up Comments on Forum for AI talk last Friday? Reading responses good overall
12 An Example
13 An Example You, as a class, act as a learning agent Actions: Wave, Stand, Clap Observations: colors, reward Goal: Find an optimal policy Way of selecting actions that gets you the most reward
14 How did you do it?
15 How did you do it? What is your policy? What does the world look like?
16 Formalizing the Activity Knowns:
17 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,...
18 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns:
19 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns: S = 4x3 grid R : S A IR P = S O T : S A S
20 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns: S = 4x3 grid R : S A IR P = S O T : S A S o i = P(s i )
21 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns: S = 4x3 grid R : S A IR P = S O T : S A S o i = P(s i ) r i = R(s i, a i )
22 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns: S = 4x3 grid R : S A IR P = S O T : S A S o i = P(s i ) r i = R(s i, a i ) s i+1 = T (s i, a i )
23 Formalizing the Activity Knowns: O = {Blue, Red, Green, Black,...} Rewards in IR A = {W ave, Clap, Stand} o 0, a 0, r 0, o 1, a 1, r 1, o 2,... Unknowns: S = 4x3 grid R : S A IR P = S O T : S A S o i = P(s i ) r i = R(s i, a i ) s i+1 = T (s i, a i )
24 PEAS description Performance measure: Environment: Actuators: Sensors:
25 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible)
26 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent
27 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent deterministic vs. non-deterministic
28 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent deterministic vs. non-deterministic episodic vs. sequential
29 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent deterministic vs. non-deterministic episodic vs. sequential static vs. dynamic
30 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent deterministic vs. non-deterministic episodic vs. sequential static vs. dynamic discrete vs. continuous
31 PEAS description Performance measure: Environment: Actuators: Sensors: fully observable vs. partially observable (accessible) single-agent vs. multiagent deterministic vs. non-deterministic episodic vs. sequential static vs. dynamic discrete vs. continuous known vs. unknown
32 Next week: Relaxing the Assumptions Textbook readings: continuous spaces, nondeterminism, partial observations, unknown environments,... Responses both Monday and Wednesday Search programming assignment
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