Chapter 8: Multi-agents Notes Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents 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) Compute the social entropy of a team. Describe the use of social rules and internal motivation for emergent social behavior. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

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

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

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

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

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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

Chapter 8: Multi-agents 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) Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

Chapter 8: Multi-agents 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 ok Dimensional score: Homogeneous Distributed control Active cooperation (though minimal) Individual goal Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Example of Heterogeneous Team USF USAR team Robot had different hardware, software Currently teleoperated 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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

Example of Heterogeneous Team USC UAV/UGV team autonomous helicopter transporting small robot Currently teleoperated Single goal, active cooperation Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Control - Centralized Control. - Distributed Control. centralized control CONTROL and distributed control regimes. In centralized control, the robots communi-cate with a central computer. The central computer distributes assignments, goals, etc., to the remote robots. The robots are essentially semi-autonomous, with the centralized computer playing the role of a teleoperator in a teleoperated system. In distributed control, each robot makes its own decisions and acts independently. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents RoboCup Robots must have a set of basic tactical behaviors but may either receive strategic commands from the central computer. Hybrid Reactive-Deliberative Paradigm, reactive layer physically resides on the robot and the deliberative layer resides on the central workstation. Distributed control is more natural for soccer playing than centralized control, because each player reacts independently to the situation. An example mid-sized league the robots are inherently heterogeneous. Although they may be physically the same, each robot is programmedwith a different role,most especially Goalie, Striker, and Defender. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Cooperation - Refers to how the robots interact with each other in pursuing a goal. - Active cooperation: Acknowledging one another and working together. ‘does not necessarily mean the robots communicate with each other.’ - Non-active cooperation: Robots individually pursue a goal without acknowledging other robots but cooperation emerges. cooperation - sensory capabilities of the robots. physical cooperation, where the robots physically aid each other or interact in similar ways. Marsupial robots are certainly a type of physical cooperation Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Box Pushing: Dynamic Reconfigurability cooperative mobility, where one robot might come over and help another robot in trouble. Shigeo Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Physically Reconfigurable Robots Or small identical robots that hook up to form a useful robot. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

Chapter 8: Multi-agents Goal Single Goal Individual Goals - purely reactive robots Single goal AAAI Compitition SRI – Saphira Coordinate with central workstation – robots responsible for autonomous navigation ----- Individual goal Ron Arkin: group of robotic space “ants”. set of behaviors: find-stationary-asteroid, move-to-asteroid, push-asteroidto- home, and avoid-robots. These behaviors give the robots individual goals, since there is no awareness of the goals of the other team members. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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

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

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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Mataric’s Nerd Herd and Social Rules with ignorant coex-istence. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

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 for time 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 The robots coexisted in a team, but did not have any knowledge of each other. A robot treated another robot as an obstacle. move-to-goal - avoid-obstacle Slow progress – traffic jam -------------------------------------------- Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents 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 it is not doing a good job: becomes increasingly FRUSTRATED and change behavior (give up) - it is called ACQUIESCENCE Allows other robots to help without task interference Robot B is finished with its task Sees that it is waiting for Robot A and becomes increasingly FRUSTRATED until it decides to help - IMPATIENCE Goes and helps Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Emotional Waiters USF heterogeneous team Robot Server, serve food, count treat removal Robot Refiller Problem in 1999: refiller blocked, Server stuck Each robot has an emotional state generator Emotions result from observed progress on task (and personality, motivation) Waiter calls for refill, if refiller takes long time, gets impatient, begins to move towards the refiller (intercept), eventually goes to refill station Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Emotions: Waiters Wait Get Intercept Go Home Hurry Refill Refiller Serve Impatient Request Waiter Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Example: Comms using ACL Waiter Refiller Serve Request Wait Refill Impatient Hurry (achieve :sender butler :receiver leguin :reply-with 1234 :language plain :ontology waiters :content refill ) Can use Agent Communication Languages such as KQML Intercept Get Intercept Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Cont. Wait Get Intercept Hurry Refill Refiller Serve Impatient Request Waiter (achieve :sender butler :receiver leguin :reply-with 1234 :language plain :ontology waiters :content hurry ) Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Cont. Wait Get Intercept Hurry Refill Refiller Serve Impatient Request Waiter (achieve :sender butler :receiver leguin :reply-with 1234 :language plain :ontology waiters :content meet me half way ) Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents Summary Many, cheap robots are 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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents

Chapter 8: Multi-agents 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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 8: Multi-agents