1. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents1 Chapter 8: Multi-agents, HRI, & Affective Computing

2. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents2 Objectives List and describe the dimensions of a multi-agent system: heterogeneity, control regime, cooperation, and goals List and describe the axes for describing a MAS task (time, subject of action, movement, dependency) List and describe the axes for describing a MAS collective (composition, size, communications, reconfigurability) Given a description of an intended task, a collection of robots, and the permitted interactions between robots, design a multi-agent system and describe the system in terms of heterogeneity, control, cooperation, and goals.

3. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents3 Objectives (cont.) Compute the social entropy of a team. Be able to program a set of homogeneous reactive robots to accomplish a foraging task. Describe the use of social rules and internal motivation for emergent social behavior. Be able to diagram the steps of robots in a team using Mataric’s social rules

4. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents4 Objectives (cont.) Given a layout of robots and tasks and a table such as Fig. 8.6 partially filled in, be able –Fill in the change in impatience and acquiescence Given a layout of robots and tasks and a table such as Fig. 8.6 with t 0 through t n : –show the next two moves and corresponding changes to the level of impatience and acquiescence of each task and the task list for robots in a team using ALLIANCE

5. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents5 The Study of Multiple Robots Distributed Artificial Intelligence Distributed Problem Solving Multi- Agent Systems

6. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents6 The Study of Agency (after Stone and Veloso 2002) Distributed Artificial Intelligence Distributed Problem Solving Multi- Agent Systems How to solve problems Or meet goals by “divide and conquer” Single computer: How to decompose task? How to synthesize solutions? Divide among agents: Who to subcontract to? How do they cooperate?

7. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents7 4 Dimensions of a Multi-agent System Heterogeneity –Same (homogeneous) vs. different (heterogeneous) –Can be different on either software or hardware Control Regime –Centralized (Phantom Menance) vs. Distributed Cooperation –Active (acknowledge each other) vs. Non-active (cooperation emerges, not explicit) –Communicating or non-communicating Goals –Single goal (same, explicit) vs. Individual

8. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents8 The Ecological Niche of a Multi- Agent System Remember…. Single Robot –Task –Environment –Agent

9. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents9 The Ecological Niche of a Multi- Agent System Multi-agent system –Task –Environment –Individual Agent –Collective emphasis

10. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents10 MAS Ecological Niche: Task (after Balch 02) There are 4 axes of a MAS Task –Time –Subject of Action –Movement –Dependency

11. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents11 4 Categories of Time 1.Fixed time –ex. Collect as many cans in 10 minutes 2.Minimum time –ex. Visit all rooms as fast as possible (minimize the time) 3.Unlimited time –ex. Patrol the building 4.Synchronization required –ex. Push two buttons at same time

12. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents12 Class Question Recall the “Call a Conference” task on the Scientific Amercian Frontiers’ Robots Alive! In that task robot(s) had to find an empty conference room, then let professors know where and when the meeting was This task falls into what category of time? –Fixed –Minimum –Unlimited –Synchronized

13. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents13 Class Question Consider the task of humanitarian demining– clearing a complex terrain of land mines- with robots. This task falls into what category of time? –Fixed –Minimum –Unlimited –Synchronized

14. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents14 2 Categories of Subject of Action Subject of Action: –Object-based robots place a single object- ex. soccer –Robot-based robots place themselves- ex. mapping

15. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents15 Soccer U-freiberg

16. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents16 Collaborative Mapping

17. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents17 Class Question Recall the “Call a Conference” task on the Scientific Amercian Frontiers’ Robots Alive! In that task robot(s) had to find an empty conference room, then let professors know where and when the meeting was This task falls into what category of subject of action? –Object-based –Robot-based

18. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents18 Class Question Consider the task of humanitarian demining– clearing a complex terrain of land mines- with robots. This task falls into what category of time? –Object-based –Robot-based

19. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents19 4 Categories of Movement 1.Coverage –Spread out to cover as much as possible 2.Convergence –Robots meet from different start positions 3.Movement-to –Going to a single location 4.Movement-while –Formation control

20. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents20 Class Question Recall the “Call a Conference” task on the Scientific Amercian Frontiers’ Robots Alive! In that task robot(s) had to find an empty conference room, then let professors know where and when the meeting was This task falls into what category of subject of action? –Coverage –Convergence –Movement-to –Movement-with

21. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents21 Class Question Consider the task of humanitarian demining– clearing a complex terrain of land mines- with robots. This task falls into what category of time? –Coverage –Convergence –Movement-to –Movement-with

22. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents22 3 Categories of Dependency 1.Independent –Robots don’t have to work directly or be aware of others 2.Dependent –Must work together for efficiency ex. Box pushing 3.Interdependent –Cyclic dependency ex. resupply

23. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents23 Box-Pushing

24. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents24 MAS Task Summary Time –Fixed time task (ex. Collect as many cans in 10 minutes) –Minimum time (ex. Visit all rooms as fast as possible) –Unlimited time (ex. Patrol the building) –Synchronization required (ex. Push two buttons at same time) Subject of Action –Object-based (e.g., robots place a single object- soccer) –Robot-based (e.g., robots place themselves- mapping) Movement –Coverage (ex. Spread out to cover as much as possible) –Convergence (ex. Robots meet from different start positions) –Movement-to (ex. Going to a single location) –Movement-while (ex. Formation control) Dependency –Independent (ex. Doesn’t require agents to know about others) –Dependent (ex. Task requires multiple agents) –Interdependent (ex. Agents depend on each other cyclically)

25. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents25 Class Question Consider the task of search and rescue, where multiple robots are to be used to search a collapsed building. Describe the task in terms of the 3 axes of a collective task –Time –Subject of action –Movement –Dependency

26. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents26 Class Question Consider the task of forensic sampling, where robots are to enter the floor a building where a crime has been committed, and then photograph and scan the entire floor as accurately as possible. Describe the task in terms of the 3 axes of a collective task –Time –Subject of action –Movement –Dependency

27. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents27 MAS Ecological Niche: Collective (after Dudek, Jenkin, and Milios 02) There are 4 axes of a collective: –Composition –Size of the collective –Communication –Collective reconfigurability

28. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents28 2 Categories of Composition Composition –Homogeneous –Heterogeneous

29. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents29 Case Studies Georgia Tech 1994 AAAI Mobile Robot Competition team Each robot hardware and software homogeneous Reactive behaviors –Wander-for-goal –Move-to-goal –Avoid –Avoid-other-robots –Grab-trash –Drop-trash Affordances –Orange=goal –Green=robot –Blue=trashcan Dimensional score: Homogeneous Distributed control Active cooperation (though minimal) Individual goal

30. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents30 Example of Heterogeneous Team USF USAR team Robot had different hardware, software Currently teleoped navigation with autonomous reactive victim detection Single goal, active cooperation –Confirm a victim with distributed sensors –Open door, “spotting” for navigation in confined spaces Dimensional score: Heterogeneous Distributed control (could be central.) Active cooperation Single goal

31. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents31 Social Entropy Way to measure heterogeneity of a collective (go to board-> 4 identical, 4 marsupial)

32. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents32 Example of Heterogeneous Team USC UAV/UGV team Currently teleoped Single goal, active cooperation

33. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents33 4 Categories of Size Size of the collective –Alone –Pair –Limited n<<than size of task or environment –Infinite n>>than size of task or environment)

34. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents34 3 Categories of Reconfigurability Collective reconfigurability –Static The organization doesn’t change, no matter what –Communicated Coordinated rearrangement Ex. Ordered to change formation –Dynamically Changes arbitrarily (esp. due to failure) Ex. A robot fails

35. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents35 Box Pushing: Dynamic Reconfigurability

36. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents36 Ex. Dynamic Reconfigurability

37. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents37 Ex. Dynamic Reconfigurability

38. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents38 Physically Reconfigurable Robots

39. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents39 5 Categories of Communication Communication (1 robot causes an external change in world that can be observed by another robot) Can minimize interference

40. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents40 5 Categories –Infinite comms are free –Motion costs as much to communicate as it would to move –ex. Box pushing (if other robot can feel the box, it’s comms) –Low comms costs more than moving from one location to another –Zero no communication between agents –Topology Broadcast, address, tree, graph

41. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents41 What Do Robots Say to Each Other? How do they “talk”? –Implicit: signaling, postures, smell –Explicit: language Who does the talking? –“the boss” -Centralized control –Everybody - Distributed control

42. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents42 What do Robots Say? (after Jung and Zelinsky 02) Communication without meaning preservation –Emitter can’t interpret its own signal –Receiver reacts in a specific way (stimulus-response) –Ex. Mating displays, bacteria emit chemicals Communication with meaning preservation –Shared common representation –Ex. Ant leaves pheromone trail to food, itself & peers can follow –Ex. Wolves leave scent markings

43. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents43 Summary: Collective Composition –Homogeneous, Heterogeneous Size of the collective –Alone, Pair, Limited, Infinite Communication –Infinite- comms are free –Motion – costs as much to communicate as it would to move –Low – comms costs more than moving from one location to another –Zero – no communication between agents –Topology Broadcast, address, tree, graph Collective reconfigurability –Static, Communicated, Dynamically

44. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents44 Class Exercise Consider the case of resupply, where many multiple vehicles are in the field and a lesser number of smaller vehicles exist to carry fuel to them, return to base, and then carry more fuel out on demand. A field vehicle emits a message that it needs to be refueled. The message intensity increases inversely proportional to the amount of remaining fuel. Describe the MAS task. Describe the MAS collective.

45. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents45 In the end…most popular Homogeneous Non-communicating agents Heterogeneous Non-communicating agents Homogeneous communicating agents Heterogeneous communicating agents

46. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents46 Class Exercise Design a multi-agent team for USAR in terms of –Heterogeneity –Control –Cooperation –Goals

47. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents47 How to Get “Right” Emergent Behavior Societal Rules vs. behaviors –Nerd Herd, Maja Mataric What if homogeneous, individual goals operating in the same area?: example-- traffic and traffic jams Motivation –ALLIANCE, Lynn Parker What if have single goal, divided among homogeneous agents and one robot breaks?: example—cleaning up a nuclear spill

48. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents48 Explicit Social Rules vs. Behaviors Societal Rules –Ignorant Coexistence Basic reactive approach, except robots couldn’t recognize other robots High degree of task interference –Informed Coexistence Recognize each other PLUS simple social rule: if detect robot, stop and wait P; if still there, turn left then resume move to goal Better –Intelligent Coexistence Recognize each other PLUS behavior: repulsed by other robots concurrent with attraction to move in same direction as the majority Best

49. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents49 Mataric’s Nerd Herd and Social Rules

50. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents50 Motivation: ALLIANCE Divide and conquer works until a robot fails; then what about the failed robot’s area Robot A fails: –It may realize that its not doing a good job: becomes increasingly FRUSTRATED and change behavior (give up) called ACQUIESCENCE Allows other robots to help without task interference –It may be clueless Other robots can help, but not as efficiently Robot B is finished with its task –Sees that waiting on Robot A, and becomes increasingly FRUSTRATED until it decides to help IMPATIENCE –Goes and helps

51. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents51 ALLIANCE

52. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents52 Summary Many, cheap robots is often better than single, expensive robot Multi-agents are generally at least reactive, sometimes hybrid deliberative/reactive Dimensions for categorizing: –Heterogeneity, control, cooperation, and goals –(may change dynamically) Interference is a big problem –Social rules –Emotions, Motivation Social entropy can be used to measure heterogeneity

53. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents53 Review Questions What are the dimensions of a multi-agent system? –Heterogeneity, control regime, cooperation, goals What are the four axes of a task in a collective? – time, subject of action, movement, dependency What are the four axes of a collective? –composition, size, communications, reconfigurability Which is more likely to fail to in the field? –a team R with 1 member of caste 1 and 5 members of caste 2 –A team R with 6 members of caste 1

54. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents54 New Material: Affective Computing Motivation for HRI Key ideas: social informatics and communication Affective computing (emotions) –Purpose of emotions –Emotions in robots Control/self-regulation Naturalistic interfaces –Examples of robots with naturalistic interfaces

55. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents55 Motivation Robots often have to team with people or work in close proximity Key questions –How to divide up responsibilities or roles? –How to change them dynamically? –How do people like to interact with robots? –How do they interact most effectively? –Do robots and people need to “understand” each other (e.g., have a shared cognitive model)?

56. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents56 HCI Human-computer interfaces (HCI) –HCI: How people interact with computers –Many sub-areas Ergonomics Human Factors Usability –Designers of computer interfaces have to have a model of what the user wants to do and their preferences/expectations in how to do it

57. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents57 HRI: Human-robot interaction Human-robot interaction (HRI) –How humans and robots interact with each other –the space in which the agent system works including the task, agents and skills, environment and conditions, social informatics, and communication. –Bi-directional HCI plus industrial organization theory Social informatics –Who has what role? When? How do they interact and change roles/responsibilities? How do the agents fit into an organization? Ex. Tool? Dog or horse? Peer? Communication –How do they communicate (verbal, signals, etc.)? What do they say to each other? When?

58. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents58 Affective Computing Affect: –phenomena manifesting itself under the form of feelings or emotions Affective computing: –Where computers take into account that users have emotions –… computers are exhibit emotions

59. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents59 Why Affective Robots? (Breazeal, Murphy) To provide naturalistic (or social) interfaces –Make interactions more enjoyable –Make interactions more natural (see Nass) –Facilitate social learning To simplify complex control issues –Emotions as Performance Monitor (and feedback) –Sorting out among multiple processes Knowing what matters Knowing what action to try Correcting errors and recognizing successes

60. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents60 Emotions: Appraisal Mechanism A mechanism for adapting to the world –Unconscious information processing of stimulus significance –Leads to a conscious, subjective experience Ex., fear –See a predator, start running –Later, say “I felt scared” Ex. Standfast in cold –Reflex is to find shelter –Emotions help adapt, overcome reflex

61. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents61 A Simplified Neurological Model (Scherer, Ortony) Amygdala Percepts (from cognitive areas) Raw Signals (from senses)

62. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents62 Can Rapidly Change Behavior Percepts (from cognitive areas) Raw Signals (from senses) Motivational-Behavioral (facial expressions, Actions) Amygdala

63. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents63 Can Cause a Physiological Response Amygdala Percepts (from cognitive areas) Raw Signals (from senses) Motivational-Behavioral (facial expressions, Actions) Somatic (endocrine system)

64. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents64 Can Lead to a “Feeling” Amygdala Percepts (from cognitive areas) Raw Signals (from senses) Motivational-Behavioral (facial expressions, Actions) Conscious-Interpretative (subjective emotion) Somatic (endocrine system)

65. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents65 Appraising What? (Ortony) Event, Agent Goals Norms/ Standards Tastes/ Attitudes Goal-Based Emotions Compound Emotions Norm-Based Emotions Taste-Based Emotions Joy/Distress Hope/Fear Relief/ Disappoint. Anger/ Gratitude Gratification/ Remorse Pride/ Shame Admiration/ Reproach Love/Hate desirability praiseworthinessappealingness

66. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents66 Subjective Nature of Emotions “Fuzzy” subjective experience More of a spectrum than a single state –Joy/distress –Hope/fear This spectrum can be broken into –Valence (where it is on the positive or negative side of the spectrum) –Intensity (what the value is: a little? A lot?)

67. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents67 Social Expression (Breazeal) We show emotions to –Share control between agents Taking turns Expressing intent Does this require a shared cognitive model? –Invoke a response Communicate intent and confirm message was sent and received

68. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents68 Kismet

69. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents69 Multilevel Process Theory of Emotions (Leventhal and Scherer, 1987) Levels: Emotional Processes Sensory-motor –Emotions modify the motor outputs of active behaviors Schematic –Emotions control which behaviors are active through prototypical schemas –Can be implemented with scripts (Lisetti 97) Conceptual –Agent reasons about emotions and projects into the future Failure to make progress on tasks/goals changes emotional state which then produces multilevel response

70. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents70 Multilevel Process Theory of Emotions (Leventhal and Scherer, 1987) Levels: Emotional Processes Sensory-motor –Emotions modify the motor outputs of active behaviors Schematic –Emotions control which behaviors are active through prototypical schemas –Can be implemented with scripts (Lisetti 97) Conceptual –Agent reasons about emotions and projects into the future Levels: Hybrid Robot Architectures Reactive behaviors –Active behaviors couple sensors and motor actions Assemblages of behaviors –Prototypical collections of behaviors are assembled into a schema or skill (Arkin 90) –Can be implemented with scripts (Murphy 96) Deliberative Planning –Can reason about past, present, and future Failure to make progress on tasks/goals changes emotional state which then produces multilevel response

71. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents71 Case Study of Multi-Process Theory: USF Waiters Hors D’euvres Anyone? Event –Cover the most area while serving food at a reception –Fully autonomous –Interact with humans Approach –Two robots, one with more sensors than the others –Sensor-endowed robot is waiter because can interact with people better –Less-endowed robot acts as a refiller, bringing trays upon request to maximize coverage by waiter –1999: people trapped refiller (deadlock

72. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents72 “Normal” Implementation BSG uses a FSA to instantiate behaviors with a set of parameters and monitors based on a causal chain, or sequence triggered by task progress (if X achieved, then B2)

73. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents73 With Emotions ESG uses a FSA to provide feedback: sensory-motor level “tweaks” parameters, while schematic level triggers alternative instantiations (escalating behavior) The set of possible behaviors remains the same, but the activation and dynamic adaptation make it more reactive and opportunistic

74. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents74 Results: Hurried Refill Claims –The TMon has indicated a condition that requires the Refiller to hurry up. –Waiter sends a “Hurry” message to Refiller Demonstrates –Refiller has a sensory-motor level change affected by a change in ESG (speeds up).

75. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents75 Timeline - Hurried Refill

76. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents76 Results: Intercept Claims –Waiter has a Schematic level change, e.g. SERVE to INTERCEPT. –TMon recognizes the “condition” that the Refiller is not going to arrive in time to avoid the Waiter having to wait without the required resource. –This “condition” leads to the Emotional state change. Demonstrates –Emotional state change from Concerned to Frustrated causes Waiter to have a schematic level change. –This modification of behavior proactively avoids deadlock situation

77. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents77 Timeline - Intercept

78. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents78 Results: GoHome Claims –Waiter has a Schematic and Sensory-motor level changes in response to Anger. Demonstrates –Waiter experiences Anger when Refiller is unable to successfully complete its assignment. Waiter essentially ‘fires’ the Refiller. Changes from SERVING to GOHOME. –Waiter avoids deadlock by completing resupply itself. –Waiter travels at the fastest speed possible.

79. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents79 Timeline - GoHome

80. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents80 Another Example: Grace Winner AAAI 2002 Challenge Problem CMU (Reid Simmons), Metrica (David Kortenkamp), NRL (Alan Schultz), Swathmore (Bruce Maxwell) Navigate from lobby, go to registration desk, ask for packet, then go and give a talk in exhibition hall Expressions generated ad hoc, no formal model of emotions

81. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents81 Other Interesting Work Affective Software Agents –Valesquez (MIT) Museum robots –Sebastian Thrun (CMU) –Illah Nourbaksh (CMU) Affective control of teams –Michaud (Sherbrooke) –Lisetti (UCF)

82. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents82 Review Questions: HRI & Affective Computing What is HRI? –How humans and robots interact with each other Define affective computing –Where computers take into account that users have emotions Give two uses of affective computing in robotics –Control or self-regulation –Naturalistic interfaces Can you have an emotional response without be conscious of it? –Yes

83. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents83 Review (Cont.) What is valence and intensity of a emotion? –Valence is where the affect is on the emotion’s spectrum –Intensity is how strong the emotion is What are the three layers of multi-level process theory of emotions? What layers in a hybrid architecture do they correspond to? –Sensorimotor, Schematic, Conceptual –Sensorimotor, Schematic -> reactive, Conceptual -> deliberative

84. 8 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents84 Review (Cont.) What did the following systems contribute in terms of emotions? –Kismet Social interface –USF Waiters Self-regulation, implementation of multi-process theory of emotions –Grace Ad hoc use of emotions for naturalistic interface