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Published on January 12, 2008

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Chapter 8: Multi-agents, HRI, & Affective Computing :  Chapter 8: Multi-agents, HRI, & Affective Computing Objectives:  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. Objectives (cont.):  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 Objectives (cont.):  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 t0 through tn: 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 The Study of Multiple Robots:  The Study of Multiple Robots Distributed Artificial Intelligence Distributed Problem Solving Multi- Agent Systems The Study of Agency (after Stone and Veloso 2002):  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? 4 Dimensions of a Multi-agent System:  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 The Ecological Niche of a Multi-Agent System:  The Ecological Niche of a Multi-Agent System Remember…. Single Robot Task Environment Agent The Ecological Niche of a Multi-Agent System:  The Ecological Niche of a Multi-Agent System Multi-agent system Task Environment Individual Agent Collective emphasis MAS Ecological Niche: Task (after Balch 02) :  MAS Ecological Niche: Task (after Balch 02) There are 4 axes of a MAS Task Time Subject of Action Movement Dependency 4 Categories of Time:  4 Categories of Time Fixed time ex. Collect as many cans in 10 minutes Minimum time ex. Visit all rooms as fast as possible (minimize the time) Unlimited time ex. Patrol the building Synchronization required ex. Push two buttons at same time Class Question:  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 Class Question:  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 2 Categories of Subject of Action:  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 Soccer:  Soccer U-freiberg Collaborative Mapping:  Collaborative Mapping Class Question:  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 Class Question:  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 4 Categories of Movement:  4 Categories of Movement Coverage Spread out to cover as much as possible Convergence Robots meet from different start positions Movement-to Going to a single location Movement-while Formation control Class Question:  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 Class Question:  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 3 Categories of Dependency:  3 Categories of Dependency Independent Robots don’t have to work directly or be aware of others Dependent Must work together for efficiency ex. Box pushing Interdependent Cyclic dependency ex. resupply Box-Pushing:  Box-Pushing MAS Task Summary:  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) Class Question:  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 Class Question:  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 MAS Ecological Niche: Collective (after Dudek, Jenkin, and Milios 02) :  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 2 Categories of Composition :  2 Categories of Composition Composition Homogeneous Heterogeneous Case Studies:  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 Example of Heterogeneous Team:  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 Social Entropy:  Social Entropy Way to measure heterogeneity of a collective (go to board-> 4 identical, 4 marsupial) Example of Heterogeneous Team:  Example of Heterogeneous Team USC UAV/UGV team Currently teleoped Single goal, active cooperation 4 Categories of Size :  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) 3 Categories of Reconfigurability :  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 Box Pushing: Dynamic Reconfigurability:  Box Pushing: Dynamic Reconfigurability Ex. Dynamic Reconfigurability:  Ex. Dynamic Reconfigurability Ex. Dynamic Reconfigurability:  Ex. Dynamic Reconfigurability Physically Reconfigurable Robots:  Physically Reconfigurable Robots 5 Categories of Communication :  5 Categories of Communication Communication (1 robot causes an external change in world that can be observed by another robot) Can minimize interference 5 Categories:  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 What Do Robots Say to Each Other? :  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 What do Robots Say? (after Jung and Zelinsky 02):  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 Summary: Collective:  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 Class Exercise:  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. In the end…most popular:  In the end…most popular Homogeneous Non-communicating agents Heterogeneous Non-communicating agents Homogeneous communicating agents Heterogeneous communicating agents Class Exercise:  Class Exercise Design a multi-agent team for USAR in terms of Heterogeneity Control Cooperation Goals How to Get “Right” Emergent Behavior:  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 Explicit Social Rules vs. Behaviors:  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 Mataric’s Nerd Herd and Social Rules:  Mataric’s Nerd Herd and Social Rules Motivation: ALLIANCE:  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 ALLIANCE:  ALLIANCE Summary:  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 Review Questions:  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 New Material: Affective Computing:  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 Motivation:  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)? HCI:  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 HRI: Human-robot interaction:  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? Affective Computing:  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 Why Affective Robots? (Breazeal, Murphy):  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 Emotions: Appraisal Mechanism:  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 A Simplified Neurological Model (Scherer, Ortony):  A Simplified Neurological Model (Scherer, Ortony) Amygdala Percepts (from cognitive areas) Raw Signals (from senses) Can Rapidly Change Behavior:  Can Rapidly Change Behavior Percepts (from cognitive areas) Raw Signals (from senses) Motivational-Behavioral (facial expressions, Actions) Amygdala Can Cause a Physiological Response:  Can Cause a Physiological Response Amygdala Percepts (from cognitive areas) Raw Signals (from senses) Motivational-Behavioral (facial expressions, Actions) Somatic (endocrine system) Can Lead to a “Feeling”:  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) Appraising What? (Ortony):  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 praiseworthiness appealingness Subjective Nature of Emotions:  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?) Social Expression (Breazeal):  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 Kismet:  Kismet Multilevel Process Theory of Emotions (Leventhal and Scherer, 1987):  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 Multilevel Process Theory of Emotions (Leventhal and Scherer, 1987):  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 Case Study of Multi-Process Theory: USF Waiters:  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 “Normal” Implementation:  “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) With Emotions:  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 Results: Hurried Refill:  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). Slide75:  Timeline - Hurried Refill Results: Intercept:  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 Slide77:  Timeline - Intercept Results: GoHome:  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. Slide79:  Timeline - GoHome Another Example: Grace:  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 Other Interesting Work:  Other Interesting Work Affective Software Agents Valesquez (MIT) Museum robots Sebastian Thrun (CMU) Illah Nourbaksh (CMU) Affective control of teams Michaud (Sherbrooke) Lisetti (UCF) Review Questions: HRI & Affective Computing:  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 Review (Cont.):  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 Review (Cont.):  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

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