Lecture Overview. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 1 1 / 15
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1 Lecture Overview What is Artificial Intelligence? Agents acting in an environment Learning objectives: at the end of the class, you should be able to describe what an intelligent agent is identify the goals of Artificial Intelligence classify the inputs and the outputs of various agents c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 1 1 / 15
2 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 2 2 / 15
3 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 3 2 / 15
4 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. An agent acts intelligently if: its actions are appropriate for its goals and circumstances c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 4 2 / 15
5 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. An agent acts intelligently if: its actions are appropriate for its goals and circumstances it is flexible to changing environments and goals c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 5 2 / 15
6 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. An agent acts intelligently if: its actions are appropriate for its goals and circumstances it is flexible to changing environments and goals it learns from experience c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 6 2 / 15
7 What is Artificial Intelligence? Artificial Intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. An agent acts intelligently if: its actions are appropriate for its goals and circumstances it is flexible to changing environments and goals it learns from experience it makes appropriate choices given perceptual and computational limitations c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 7 2 / 15
8 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 8 3 / 15
9 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 9 3 / 15
10 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... Computers/devices thermostat, user interface, airplane controller, network controller, game, advising system, tutoring system, diagnostic assistant, robot, Google car, Mars rover... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 10 3 / 15
11 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... Computers/devices thermostat, user interface, airplane controller, network controller, game, advising system, tutoring system, diagnostic assistant, robot, Google car, Mars rover... Animals dog, mouse, bird, insect, worm, bacterium, bacteria... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 11 3 / 15
12 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... Computers/devices thermostat, user interface, airplane controller, network controller, game, advising system, tutoring system, diagnostic assistant, robot, Google car, Mars rover... Animals dog, mouse, bird, insect, worm, bacterium, bacteria... book(?), sentence(?), word(?), letter(?) c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 12 3 / 15
13 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... Computers/devices thermostat, user interface, airplane controller, network controller, game, advising system, tutoring system, diagnostic assistant, robot, Google car, Mars rover... Animals dog, mouse, bird, insect, worm, bacterium, bacteria... book(?), sentence(?), word(?), letter(?) Can a book or article do things? c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 13 3 / 15
14 Examples of Agents Organisations Microsoft, Al Qaeda, Government of Canada, UBC, CS Dept,... People teacher, physician, stock trader, engineer, researcher, travel agent, farmer, waiter... Computers/devices thermostat, user interface, airplane controller, network controller, game, advising system, tutoring system, diagnostic assistant, robot, Google car, Mars rover... Animals dog, mouse, bird, insect, worm, bacterium, bacteria... book(?), sentence(?), word(?), letter(?) Can a book or article do things? Convince? Argue? Inspire? Cause people to act differently? c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 14 3 / 15
15 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 15 4 / 15
16 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. analyze natural and artificial agents c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 16 4 / 15
17 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. analyze natural and artificial agents formulate and test hypotheses about what it takes to construct intelligent agents c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 17 4 / 15
18 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. analyze natural and artificial agents formulate and test hypotheses about what it takes to construct intelligent agents design, build, and experiment with computational systems that perform tasks that require intelligence c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 18 4 / 15
19 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. analyze natural and artificial agents formulate and test hypotheses about what it takes to construct intelligent agents design, build, and experiment with computational systems that perform tasks that require intelligence Engineering goal: design useful, intelligent artifacts. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 19 4 / 15
20 Goals of Artificial Intelligence Scientific goal: to understand the principles that make intelligent behavior possible in natural or artificial systems. analyze natural and artificial agents formulate and test hypotheses about what it takes to construct intelligent agents design, build, and experiment with computational systems that perform tasks that require intelligence Engineering goal: design useful, intelligent artifacts. Analogy between studying flying machines and thinking machines. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 20 4 / 15
21 Agents acting in an environment Abilities Goals/Preferences Prior Knowledge Agent Stimuli Past Experiences Actions Environment c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 21 5 / 15
22 Inputs to an agent Abilities the set of possible actions it can perform Goals/Preferences what it wants, its desires, its values,... Prior Knowledge what it comes into being knowing, what it doesn t get from experience,... History of stimuli (current) stimuli what it receives from environment now (observations, percepts) past experiences what it has received in the past c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 22 6 / 15
23 Example agent: autonomous car abilities: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 23 7 / 15
24 Example agent: autonomous car abilities: steer, accelerate, brake c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 24 7 / 15
25 Example agent: autonomous car abilities: steer, accelerate, brake goals: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 25 7 / 15
26 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 26 7 / 15
27 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 27 7 / 15
28 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: what signs mean, what to stop for c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 28 7 / 15
29 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: what signs mean, what to stop for stimuli: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 29 7 / 15
30 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: what signs mean, what to stop for stimuli: vision, laser, GPS... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 30 7 / 15
31 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: what signs mean, what to stop for stimuli: vision, laser, GPS... past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 31 7 / 15
32 Example agent: autonomous car abilities: steer, accelerate, brake goals: safety, get to destination, timeliness,... prior knowledge: what signs mean, what to stop for stimuli: vision, laser, GPS... past experiences: streetmaps, how breaking, steering affects direction.. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 32 7 / 15
33 Example agent: robot abilities: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 33 8 / 15
34 Example agent: robot abilities: movement, grippers, speech, facial expressions,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 34 8 / 15
35 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 35 8 / 15
36 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 36 8 / 15
37 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 37 8 / 15
38 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: what is important feature, categories of objects, what a sensor tell us,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 38 8 / 15
39 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: what is important feature, categories of objects, what a sensor tell us,... stimuli: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 39 8 / 15
40 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: what is important feature, categories of objects, what a sensor tell us,... stimuli: vision, sonar, sound, speech recognition, gesture recognition,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 40 8 / 15
41 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: what is important feature, categories of objects, what a sensor tell us,... stimuli: vision, sonar, sound, speech recognition, gesture recognition,... past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 41 8 / 15
42 Example agent: robot abilities: movement, grippers, speech, facial expressions,... goals: deliver food, rescue people, score goals, explore,... prior knowledge: what is important feature, categories of objects, what a sensor tell us,... stimuli: vision, sonar, sound, speech recognition, gesture recognition,... past experiences: effect of steering, slipperiness, how people move,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 42 8 / 15
43 Example agent: teacher abilities: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 43 9 / 15
44 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 44 9 / 15
45 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 45 9 / 15
46 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 46 9 / 15
47 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 47 9 / 15
48 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: subject material, teaching strategies,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 48 9 / 15
49 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: subject material, teaching strategies,... stimuli: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 49 9 / 15
50 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: subject material, teaching strategies,... stimuli: test results, facial expressions, errors, focus,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 50 9 / 15
51 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: subject material, teaching strategies,... stimuli: test results, facial expressions, errors, focus,... past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 51 9 / 15
52 Example agent: teacher abilities: present new concept, drill, give test, explain concept,... goals: particular knowledge, skills, inquisitiveness, social skills,... prior knowledge: subject material, teaching strategies,... stimuli: test results, facial expressions, errors, focus,... past experiences: prior test results, effects of teaching strategies,... c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 52 9 / 15
53 Example agent: thermostat for heater abilities: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
54 Example agent: thermostat for heater abilities: turn heater on or off c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
55 Example agent: thermostat for heater abilities: turn heater on or off goals: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
56 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
57 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
58 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: 24 hour cycle, weekends c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
59 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: 24 hour cycle, weekends stimuli: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
60 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: 24 hour cycle, weekends stimuli: temperature, set temperature, who is home, outside temperature c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
61 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: 24 hour cycle, weekends stimuli: temperature, set temperature, who is home, outside temperature past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
62 Example agent: thermostat for heater abilities: turn heater on or off goals: conformable temperature, save fuel, save money prior knowledge: 24 hour cycle, weekends stimuli: temperature, set temperature, who is home, outside temperature past experiences: when people come and go, who likes what temperature c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
63 Example agent: medical doctor abilities: goals: prior knowledge: stimuli: past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
64 Example agent: Apple Inc. abilities: goals: prior knowledge: stimuli: past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
65 Other Agents user interface bee smart home... abilities: goals: prior knowledge: stimuli: past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
66 Example agent: abilities: goals: prior knowledge: stimuli: past experiences: c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
67 Agents acting in an environment Abilities Goals/Preferences Prior Knowledge Agent Stimuli Past Experiences Actions Environment c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page / 15
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