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May 14, 2008 1
Organization Design and Dynamic Resources
Huzaifa Zafar
Computer Science Department
University of Massachusetts, Amherst
May 14, 2008 2
Organization Design The organization of a multi-agent system is the collection of roles, relationships, and authority structures which govern its behavior - [Horling & Lesser 05]
Organization Design v/s Operational Design
Long Term v/s Short term
Used to guide Data Flow Resource Allocation Coordination Pattern … etc
May 14, 2008 3
Dynamic Resources Dynamic Resources are those resources where some characteristics of the resource changes over time
Example - Network Routing Cost of communication changes as network loads change
Paths in multi-hop communication changes as links fail
Environmental interference changes over time
Example 2 - Battery Power Consumption More usage of power implies faster battery consumption
Less available power implies an agent can take up less responsibility.
May 14, 2008 4
Outline How can we make better use of resource allocation given knowledge of the Organization design?
Network Routing eCQRouting Experimental Analysis
How can we redesign/adapt our organization to the changing resource?
Problem setup Challenges we face in solving this problem Example applications
May 14, 2008 5
Motivation
Application
Layer
Application
Layer
Network
Layer
Network
Layer
Agent A Agent B
Application
Message
Application
Message
Organization Knowledge, Message priority
Effect of message loss on performance
May 14, 2008 6
Introduction Objectives:
Significant number of network exploration messages required to support multi-hop communication
In turn reduces available bandwidth for application messages
Reduce this number in order to increase application level bandwidth
Further regulate the number of exploration messages based on:
Priority of messages Relationship between rate of message loss and performance
Use application level organizational estimates of direction and priority of communication in network level routing protocols
May 14, 2008 7
Routing At each time step do:
Each destination-agent sends out an exploration message
All other agents in the network receive this exploration message and use the corresponding time delay to predict cost of sending messages to the corresponding destination
Agents develop policies for sending messages based on costs
Policy dictates next hop when multi-hop routing Cost of sending exploration messages? eCQRouting: At each time step do:
Should I as the destination-agent send a message?
How much confidence do I as a source-agent have on the policies?
May 14, 2008 8
eCQRouting:Organizational Input
Direction and priority of communication Effect of message loss on performance Minimum path-confidence Exploration-decision frequency Learning rate (α ) - For Q-Learing
May 14, 2008 9
eCQRouting Step 1 Each agent has access to a weighted graph representing direction and priority of communication between agent roles in the network
No network-level topological information Use the graph to determine if an agent is a destination-agent {Cluster-Head and Regional-Agents}.
All agents are source-agents
May 14, 2008 10
Example NetworkSensor Agent
Regional Node
Cluster Head
Data Messages
Exploration Messages
Exploration messages are sent along with Data messages, causing interference and reduction in bandwidth
May 14, 2008 11
eCQRouting Step 2.1: source-agent
Uses time delay in receiving exploration messages along with Q-Learning to determine local policies
The policy of an agent determines the next best hop to a given destination
Confidence represents how well the Q-Value reflects the current state of the network
Confidence degrades with time in the absence of exploration messages
Calculated at source: The lower the confidence of an agent, the less its
Q-Values (and in turn policies) change with updates
Time delay in receiving exploration messages Current Confidence in Q-ValueLearning rate
May 14, 2008 12
eCQRouting Step 2.2:destination-agent
Exploration Objective: Determine the cost of sending a message from a source
Every cycle: Regulate this threshold depending on the organization (later in this talk)
Confidence has dropped below a threshold
A minimum path-confidence threshold is provided as input
Source agent communicates its confidence
Source-agents use exploration messages to estimate time required to sending application messages to the destination
May 14, 2008 13
Example Network
Benefits : Lower number of exploration messages
Exploration messages are of a smaller size
Q-Table is smaller
May 14, 2008 14
eCQRouting Step 2.3: Exploration based on message priority
More frequent exploration by high priority destinations (messages to the corresponding destination have high priority)
Destination agent changes threshold depending on message priority
Q-Values of application messages to high priority destinations are more accurate, with low priority messages less accurate.
May 14, 2008 15
eCQRouting Step 2.4:Exploration based on message loss
Source agents: Determine the rate of message loss to the destination Send message loss rate to the destination
Destination agents: Explore more frequently when current paths have significant application-level performance degradation
Agents tolerate high message-loss rates if the corresponding performance degradation is low
May 14, 2008 17
CNASCollaborative Network for Atmospheric Sensing
Power-Aware, Agent-Based nodes Hierarchical Organization Sensor Agents collect data Cluster Heads aggregate data and guide sensor agents
Cluster Heads send aggregated data to regional agents
May 14, 2008 18
CASA - Collaborative Adaptive Sensing of the Atmosphere
Considerably higher bandwidth requirement than CNAS
4 Roles; Radars, Feature Detectors, Feature Repositories and Optimizers
Roles higher in the hierarchy communicate with higher priority
May 14, 2008 19
Experiment - Bandwidth Increase
Networks range from 4 agents to 100 agents Agents are randomly placed such that density remains constant as network size increases
1 Cluster Head for every 3 Sensor Agents; placed randomly in the network
35% additional application bandwidth in the network of size 100 when compared to OLSR
May 14, 2008 20
Experiment - Robust Performance
Network of 160 agents 4 Optimizer agents; 4 Feature-Repository/Feature-Detector agents; Rest Radar agents
More robust performance degradation with message loss
Insignificant difference between the two threshold modification algorithms
May 14, 2008 21
Conclusions Reduce network exploration messages
Selected agents explore depending on organization knowledge
Each agent explores only if the confidence in Q-Value of the path is below a threshold
Regulate path-confidence threshold Priority of messages - high priority destinations explore more often
Effect of message loss on performance - Significant effect implies more exploration to find alternative paths
May 14, 2008 22
Future Work - Problem Setup
Resource - Network Routing Given - A basic organization
Question 1: How is this organization represented?
Wireless Networks Cost of sending messages fluctuate regularly
Adhoc Networks Agents enter and leave the network dynamically
Agent Failures Agents are unable to communicate with their neighbors
Emergent Organization?
May 14, 2008 23
Challenges Effect of change in organization on the network
Message interferences Changes in costs with changes in traffic Effects of mobility of nodes
Goodness of Organization How do we determine if one organization is better than another organization?
Cost of evaluating the organization Effect of time spent evaluating on the MAS
Reorganization/Adaptation costs Time spent in developing the new organization Cost of updating all agents with the new organization
May 14, 2008 24
Experimental Analysis Reorganizing CNAS
Re-ordering the leader agent priority lists Regional nodes
RoboRescue Fire Hazards
Organizing agents based on locations of fire hazards Predicting (or detecting) environmental changes
Communication Costs Reorganizing to reduce communication costs/limitations