The Role of Organizational Control in Scaling AI Systems
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Transcript of The Role of Organizational Control in Scaling AI Systems
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The Role of Organizational The Role of Organizational Control in Scaling AI Control in Scaling AI
SystemsSystems
Victor R. LesserVictor R. Lesser
Computer Science DepartmentComputer Science DepartmentUniversity of Massachusetts, AmherstUniversity of Massachusetts, Amherst
IJCAI 2009IJCAI 2009Pasadena, California Pasadena, California
July 16, 2009July 16, 2009
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ThanksThanks Raj Reddy – for his support, encouragement Raj Reddy – for his support, encouragement
and mentoringand mentoring Lee Erman – my early colleague and closest Lee Erman – my early colleague and closest
friend for over 40 yearsfriend for over 40 years My wonderful graduate students – for their My wonderful graduate students – for their
creativity, hard work and collegialitycreativity, hard work and collegiality A special thanks to my first graduate student, A special thanks to my first graduate student,
Dan CorkillDan Corkill Multi-Agent Systems community – who have Multi-Agent Systems community – who have
been a welcoming homebeen a welcoming home My wife and children – who have created a My wife and children – who have created a
richness in my personal liferichness in my personal life
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OutlineOutline BackgroundBackground Examples of Organizational ControlExamples of Organizational Control
Distributed sensor networksDistributed sensor networks Distributed search in a peer-to-peer IRDistributed search in a peer-to-peer IR Multi-agent reinforcement learning for Multi-agent reinforcement learning for
distributed resource allocation distributed resource allocation
What are the Major Research TopicsWhat are the Major Research Topics SummarySummary
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How to Construct Societies of How to Construct Societies of Sophisticated AI Systems that Sophisticated AI Systems that
Work Together EffectivelyWork Together Effectively
• Limited BandwidthLimited Bandwidth
• Lack of Global ViewLack of Global View
• Decentralized ControlDecentralized Control
• Autonomous, Autonomous, Asynchronous Asynchronous SubsystemsSubsystems
• Need for CooperationNeed for Cooperation
Why is this AI rather than Distributed Systems?
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Why This Model for Building Why This Model for Building Intelligent Systems vs. Intelligent Systems vs. A Monolithic Approach?A Monolithic Approach?
Geographical Distribution of Information, Geographical Distribution of Information, Resources, Expertise Resources, Expertise Privacy in sharing information, fee-based servicesPrivacy in sharing information, fee-based services
Modularity for Ease of Development, Modularity for Ease of Development, Debugging, Modification, EvolutionDebugging, Modification, Evolution
Do we need to add in some picture of Do we need to add in some picture of an example system -- Coordinatorsan example system -- Coordinators
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What is the Control Problem What is the Control Problem
Managing Interdependencies among Managing Interdependencies among
Agent ActivitiesAgent Activities What tasks to do, when, where, howWhat tasks to do, when, where, how
What information to communicate, What information to communicate, when, to whomwhen, to whom
How to do this in a globally optimal wayHow to do this in a globally optimal way
Difficulty
Limited communication and computational resources
Ubiquity of uncertainty – uncertain, out-of-date, incomplete information
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A Model for A Model for Computation in the 21Computation in the 21stst
CenturyCentury
Scaling to 100’s to 1000’s of agentsScaling to 100’s to 1000’s of agents Complex organizational relationships Complex organizational relationships
among agentsamong agents Organizationally situated agentsOrganizationally situated agents
Operate in a “satisficing” modeOperate in a “satisficing” mode Managing uncertainty as an integral Managing uncertainty as an integral
part of network problem solvingpart of network problem solving Highly adaptive and reliableHighly adaptive and reliable
Network of cooperating, intelligent agents Network of cooperating, intelligent agents (people/machines)(people/machines)
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What is the Lecture What is the Lecture AboutAbout
Organizational control as one way to Organizational control as one way to approach the scaling of AI Systemsapproach the scaling of AI Systems Organizational control is a multi-level Organizational control is a multi-level
approach in which long-term approach in which long-term organizational goals and roles are used as organizational goals and roles are used as guidelines for agents’ detailed operational guidelines for agents’ detailed operational decisionsdecisions. .
Presenting interesting research topics Presenting interesting research topics associated with organizational controlassociated with organizational control
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Multi-Layer Control Multi-Layer Control Approach Approach
Organizational ControlOrganizational Control Global and long-term perspective Global and long-term perspective
on system performance on system performance Long-term (a-temporal) directivesLong-term (a-temporal) directives
Operational ControlOperational Control Limited and dynamic perspectiveLimited and dynamic perspective Short-term (temporal) decision in Short-term (temporal) decision in
the context of organizational the context of organizational directivesdirectives
ORGANIZATIONAL
GROUP
LOCAL
Approximate, Distributed Optimization for the Global Control Problem
Organizational ControlOrganizational Control
Organizational Design
Guidelines, Roles, Goals...etc.
• Communication and action spaces limited by roles
• Reward functions modified to prefer certain actions aids in coordination
• Global reward function broken into limited-locality reward components
MDP- based operational decisions
Organization-Aware Operational Control
MDP Agent
= Group-coordinatio
n action
Why Does Organizational Why Does Organizational Control WorkControl Work
Exploits Repetitive and Nearly-Exploits Repetitive and Nearly-Decomposable Nature of Problem SolvingDecomposable Nature of Problem Solving
Exploits Knowledge of the EnvironmentExploits Knowledge of the Environment Expectations of Task Arrivals, Problem-Solving Expectations of Task Arrivals, Problem-Solving
Behavior and OutcomesBehavior and Outcomes
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Efficiency through Assumptions
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Drivers for Organization FocusDrivers for Organization Focus“Bounded Rationality”“Bounded Rationality”
Organizational Control provides a framework Organizational Control provides a framework for dealing with computational issues of scalefor dealing with computational issues of scale
Decrease non-local information and reasoning Decrease non-local information and reasoning
necessarynecessary
Acting in accordance with guidelines leads to Acting in accordance with guidelines leads to effective coordination decisionseffective coordination decisions
Shift from an Agent-Centric, Operational Shift from an Agent-Centric, Operational View of Coordination to an Organization-View of Coordination to an Organization-
Centric OneCentric One
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Example System Example System - 1- 1
Adaptive, Real-Time Adaptive, Real-Time Distributed Sensor Distributed Sensor
Network (2004)Network (2004)
(Bryan Horling, Roger Mailler, Regis (Bryan Horling, Roger Mailler, Regis Vincent)Vincent)
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DARPA: Distributed Sensor DARPA: Distributed Sensor Network Challenge ProblemNetwork Challenge Problem•Small 2D Small 2D
Doppler radar Doppler radar units (30’s)units (30’s)– Scan Scan one of one of
threethree 120 120 sectors at a sectors at a timetime
• Commodity Commodity processor processor associated associated with each with each radarradar
•Communicate Communicate short short messages messages using one of 8 using one of 8 radio channelsradio channels
•Triangulate Triangulate radars to do radars to do trackingtracking
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CASA - Monitoring for CASA - Monitoring for Severe Weather (2008)Severe Weather (2008)
Network of short-range (30 Network of short-range (30 km), overlapping, adaptive km), overlapping, adaptive weather-sensing radarsweather-sensing radars SSmall fielded system in mall fielded system in
OklahomaOklahoma
Goal: Detect low-lying weather Goal: Detect low-lying weather phenomena such as tornadoes phenomena such as tornadoes within 60 secondwithin 60 second
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How to Control the DSNHow to Control the DSN
ScalabilityScalability: Hundreds of sensors, multiple : Hundreds of sensors, multiple targets, constrained communicationtargets, constrained communication
What if there were no (formal) organization?What if there were no (formal) organization? Who decides if a target is new?Who decides if a target is new? Who tracks a target?Who tracks a target? How do trackers obtain sensor information?How do trackers obtain sensor information?
These operational control decisions could be These operational control decisions could be made individually by each agent, but through made individually by each agent, but through organization can be made easierorganization can be made easier
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DSN Organizational DSN Organizational ControlControl
Partitioned Partitioned EnvironmentEnvironment SectorsSectors Constrains info. Constrains info.
propagationpropagation Reduces Reduces
information loadinformation load Exploits localityExploits locality
Agents assigned Agents assigned rolesroles Sensor Sensor
(Scan/Track) (Scan/Track) Sector ManagerSector ManagerTrack ManagerTrack Manager
Limits sources of Limits sources of informationinformation
Facilitates data Facilitates data retrievalretrieval
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Partitioning of NodesPartitioning of Nodes
• The environment is first partitioned into sectors.The environment is first partitioned into sectors.• Sector managers are then assignedSector managers are then assigned..
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Competition for Sensor Competition for Sensor AgentsAgents
• Sector members send their capabilities to their managers.Sector members send their capabilities to their managers.• Each manager then generates and disseminates a scan schedule.Each manager then generates and disseminates a scan schedule.
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SRTA: SRTA: Soft Real-Time Agent Soft Real-Time Agent
ArchitectureArchitecture Mapping Org and Mapping Org and
Dynamic Coordination Dynamic Coordination Guidelines into Guidelines into Operational DecisionsOperational Decisions
Guidelines into detailed Guidelines into detailed resource allocationsresource allocations
Resolve conflicts locally Resolve conflicts locally not resolvednot resolved
Resource Modeler
Conflict Resolution Module
Task Merging
Problem solver
Periodic Task Controller
TÆMS Library
Cache Check
DTC-Planner
Partial Order Scheduler
Parallel Execution Module
Learning
UpdateCache
CacheHit
Linear Plan
TAEMS-Plan Network/Objective
Goal Description/Objective
Parallel ScheduleScheduleFailure
Results
Update Expectations
Schedule Failure
OtherAgents
Schedule
Resource Uses
MultipleStructures
Negotiation(e.g. SPAM)
Commitments/Decommitments
Schedule failure/Abstract view
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Track Manager SelectionTrack Manager Selection
• Nodes in the scan schedule perform scanning actions.• Detections reported to Manager and a Track Manager selected.
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Managing Conflicted Managing Conflicted ResourcesResources
• Track Manager discovers and coordinates with tracking nodes.• New tracking tasks may conflict with existing tasks at the node.
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Mediator ViewMediator ViewInterdependency GraphInterdependency Graph
SPAM: Mediation-Based Negotiation
M20M33
M0 M8
M7
M25M14
S15
S32
S53S18
S25,S20
S7
S18
S5 S8
S12, S22
S2, S14
M20M33
M8
M7
M25
1
1
11
2
1
1
1
World View- Multi-Linking of Resource Allocations
World View- Multi-Linking of Resource Allocations
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What Does What Does Organizational Control Organizational Control
AccomplishAccomplish Managing Resource ContentionManaging Resource Contention
Sensors, processors, communicationSensors, processors, communication Centralizing Information in Sector Centralizing Information in Sector
ManagerManager Handling data correlation with multiple Handling data correlation with multiple
trackstracks Fault ToleranceFault Tolerance Communication Locality for TrackingCommunication Locality for Tracking
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DeviationDeviation
(high = bad)(high = bad)
RMS ErrorRMS Error
(low = good)(low = good)
Organizational Trade-Organizational Trade-OffsOffs
How big should sectors be?How big should sectors be? Empirical evidence: between 5-10 Empirical evidence: between 5-10
sensorssensors This would vary, depending on sensor This would vary, depending on sensor
and environmental characteristicsand environmental characteristics
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Example System Example System - 2- 2
Information Information Retrieval in a Peer-Retrieval in a Peer-
to-Peer Network to-Peer Network (2007)(2007)
(Haizheng Zhang, Bryan (Haizheng Zhang, Bryan Horling)Horling)
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Information Retrieval Information Retrieval in a Peer-to-Peer in a Peer-to-Peer
NetworkNetwork
American Patent DB Wall Street JournalAssociated Press News
Insider trading Insider trading stories?stories?
A
B
C
D E
F
GI
H
J
K
Generate incrementallyand distributively an appropriateorganization for effective retrieval
Problem DescriptionProblem Description::
1. Improve IR performance2. System performance
GoalGoal:
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Organization for Peer-to-Organization for Peer-to-Peer Content RetrievalPeer Content Retrieval
Initial and Initial and Unstructured Peer-to-Unstructured Peer-to-
Peer NetworkPeer Network
Nearly-Decomposable Nearly-Decomposable Hierarchy of Content Hierarchy of Content
MediatorsMediators
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Content-Based Hierarchical Content-Based Hierarchical Agent OrganizationsAgent Organizations
Group agents of similar contentGroup agents of similar content Limit subset of agents to be probedLimit subset of agents to be probed Add lateral links to quickly locate diverse Add lateral links to quickly locate diverse
contentcontent
Incremental construction of the organization Incremental construction of the organization as new agents join networkas new agents join network
A two-phase search algorithmA two-phase search algorithm Locate relevant hierarchical agent clusters Locate relevant hierarchical agent clusters Perform searches in clustersPerform searches in clusters
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Two-Phase Search ProtocolTwo-Phase Search Protocol
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Internal Agent StructureInternal Agent Structure
Search Engine
DocumentCollection
Local Search
Agent Control Unit Agent
View
Neighbor 1
Neighbor 2
Neighbor n
...
QrsQrs
QlsQls
Q1Q1
Q2Q2
QnQn
Load Balance Unit
...
qqikik
qqjkjk
qqkk
qqkk
Resource Selection
Local Queries
qk1
qqknkn
qqk2k2
q'k1
q'q'knkn
q'q'k2k2
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Experimental Results (Search Experimental Results (Search Quality versus Number of Quality versus Number of
Messages)Messages)(TREK 921 Nodes)(TREK 921 Nodes)
Message Number and Search Quality
0
500000
1000000
1500000
2000000
2500000
3000000
Categories
Categories
Mes
sage
Num
ber
0
102030
405060
708090
100
Sear
ch Q
uality Message Number
Search Quality
PBN PBY HTN HTY HBN HBY
Hierarchical BalancedWith Load Control
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Example System Example System - 3- 3
Distributed Distributed Resource Allocation Resource Allocation for Computational for Computational
ServicesServices(2009)(2009)
(Chongjie Zhang, Sherief (Chongjie Zhang, Sherief Abdallah)Abdallah)
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Integrate Organizational Integrate Organizational Control into Multi-Agent Control into Multi-Agent
LearningLearning Convergence in large-scale settings is Convergence in large-scale settings is
challenging — speed, likelihood and challenging — speed, likelihood and quality.quality.
Non-stationary Non-stationary learning learning environmentenvironment
Partial view and no Partial view and no global reward global reward signalsignal
Communication Communication delaydelay
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Organization-Based Organization-Based Control FrameworkControl Framework
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Integrate Supervisory Integrate Supervisory Information into Multi-Information into Multi-
Agent LearningAgent Learning
Policy Update
Action Selection
Policy
Reward Action State Policy Update
Action Selection
Policy
Reward Action State
Supervisory Policy
Adaptation
ReportGenerator
Rules andSuggestions
Adapted Policy
Abstracted state and reward
(a) Multi-Agent Reinforcement Learning (MARL) (b) MARL under Supervision
Reward
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Experiments: Distributed Experiments: Distributed Task Allocation Problem Task Allocation Problem
(729 agents)(729 agents)
27 X 27 Agent Network
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What Do These Examples What Do These Examples Tell UsTell Us
Organizational Control can be used in Organizational Control can be used in scaling of very different types of AI scaling of very different types of AI problem solvingproblem solving
Flexibility and adaptability of control Flexibility and adaptability of control decisions at all levels is importantdecisions at all levels is important
Very early in our understanding of how to Very early in our understanding of how to effectively exploit this approacheffectively exploit this approach
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How to Create an How to Create an OrganizationOrganization
Top-DownTop-Down
Emergent / Emergent / Self-OrganizingSelf-Organizing
Some CombinationSome Combination
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What Constitutes an What Constitutes an OrganizationOrganization
What is the Role of Institutional What is the Role of Institutional MechanismsMechanisms Computational artifacts for controlComputational artifacts for control
What Type of AgentsWhat Type of Agents CooperativeCooperative Self-interestedSelf-interested Semi-cooperativeSemi-cooperative
What is an Organizationally Situated AgentWhat is an Organizationally Situated Agent
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Relationship between MAS and Relationship between MAS and Organizational Structuring from a Organizational Structuring from a business/sociological perspective?business/sociological perspective?
Are emotions effective computational Are emotions effective computational
mechanisms?mechanisms? Skepticism – limits effect of info distraction Skepticism – limits effect of info distraction Boredom – avoid over-learning of routine Boredom – avoid over-learning of routine
taskstasks Self-interest – decision making without Self-interest – decision making without
global impact global impact
MAS and Human MAS and Human OrganizationsOrganizations
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Can you Automate the Can you Automate the Organizational Design Organizational Design
Process?Process? Theory behind organizational designTheory behind organizational design
The nature of sub-problem interdependenciesThe nature of sub-problem interdependencies
Designing for multi-attributed nature of Designing for multi-attributed nature of organizational performanceorganizational performance Reliability, fail-softness, adaptabilityReliability, fail-softness, adaptability
Predicting the performance of a Predicting the performance of a computational organizationcomputational organization
Specialness of the search process for Specialness of the search process for finding a good organizationfinding a good organization Repetitiveness of structureRepetitiveness of structure
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The Human in the LoopThe Human in the Loop
How can computational organizations How can computational organizations be controlled by peoplebe controlled by people
How can human and computational How can human and computational organizations interactorganizations interact
What is the implication for how we see What is the implication for how we see ourselves and others ourselves and others
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SummarySummary
Organizational Control is Organizational Control is important in how we think about important in how we think about scaling AI systemsscaling AI systems
Organizational Control is an Organizational Control is an intrinsically interesting problem intrinsically interesting problem that deserves our intellectual that deserves our intellectual attentionattention