a multi-agent infrastructure for mobile workforce management in a service oriented enterprise
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A Multi-Agent Infrastructure for Mobile Workforce Management
in a Service Oriented Enterprise
Shing-Chi CHEUNGSenior Member, IEEE
Dept. of Computer Science Hong Kong University of
Science & Technologyscc@cs.ust.hk
Ho-fung LEUNGSenior Member, IEEE
Dept. of Computer Science & Engineering
The Chinese University of Hong Kong
lhf@cuhk.edu.hk
Dickson K. W. CHIUSenior Member, IEEE
Dickson Computer SystemsHong Kong
dicksonchiu@ieee.org
Mobile Workforce
Mgmt HICSS38-2
Introduction
Agents - programs that act on behalf of their human users and exhibit some aspects of autonomous behavior
Multi-agent information system (MAIS) used to conduct e-commerce activities
Support ubiquitous access through mobile devices over wired and wireless networks
As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence also subject to mobility => peer-to-peer mobile computing
Relevant domain for mobile workforce management - supply-chain logistics, group calendars, dynamic human resources planning and postal services
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Motivation for Mobile Workforce Management (MWM)
MWM typically involves tight collaboration, negotiation, and sophisticated business domain knowledge.
Integration of disparate business function for its mobile professional workforce and the management with a unified infrastructure
Provision of personalized assistance and automation => Agents
Service Oriented Enterprise professional workforce (engineers, medical professionals) diversified capabilities, personal preferences, professional
requirements existing solutions and proposals often treat the workforce
as passive moving resources and cannot cope with current requirements for knowledge based economy and services
MAIS Infrastructure
Mobile Workforce
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Background of Research
Constraint based negotiation (e.g., meeting scheduling) – “Constraint-based Negotiation in a Multi-Agent Information System with Multiple Platform Support”, HICSS37, Jan 2004.
M-service adaptation – “A Three-Tier View Methodology for adapting M-services,” IEEE TSMC, Part A, 33(6):725-741, 2003
Alert Management – “Alert Driven E-Services Management”, HICSS37, Jan 2004.
Generalize and consolidate our experience and apply to this new field – MWM for service oriented enterprise
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Layered Infrastructure for MWM
-EIS 3-Tier Implementation Architecture(Interface Tier / Application Tier / Data Tier)
BDI Agent
Collaboration Protocol
MAIS(Multi-Agent Information System)
Mobile Workforce Management
Mobile Workforce
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Meta-model of an MAIS
Function
Data Set
Input/Output Collaboration Protocol
Application Tier
Front Tier
Data Tier
Three Tier System11
11
11
implements
implements
implements
implements
Preference
Organization
11
hosts
User
*
1
*
BDI Agent
**
conforms to
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1
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MAIS Analysis and Design Methodology for MWM
Part 1 - overall architectural design High-level requirements Formulate an enterprise MAIS infrastructure System integration aspects Specific to a particular purpose (MWM here) Address a particular domain (service oriented
enterprises here) Focus of paper
Part 2 - detailed design of agents Proceed after a successful high-level requirement
studies Each types of agents in the MWM domain has high
potentials for further in depth research because of its emerging adoptions
See our paper in HICSS37 NSS track
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MAIS Overall Architectural Design
Identify different categories of services and objectives for workforce in the enterprise.
Identify the lifecycle (i.e., different phases) for the management of typical service task, from task request to completion.
For each phase, identify the major agent to represent each of them and then the interactions required among them based on the process requirements.
Further identify minor agents that assist the major agents to carry out these functionalities. As a result, clusters of different types of agents (instead of a single monolithic pool of agents) constitute the MAIS.
Identify the interactions required for each minor agent type. Design the basic logics for all these agents. Identify the (mobile) platforms to be supported and where to
host different types of agents. See if any adaptation is required.
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Detailed Design of Agents Design and adapt the user interface required for users
to input their preferences. Customize displays to individual users and platforms.
Determine how user preferences are mapped into constraints and exchange them in a standardized format.
Now, we can consider automated decision support with agents. Identify the stimulus, collaboration parameters, and output actions to be performed by a BDI agent.
Partition the collaboration parameters into three data sets: belief, desire, and intention. Formulate a data sub-schema for each of these data sets. Implement the schema at the data tier.
Derive transformations amongst the three data sets. Implement these transformations at the application tier.
Enhance the performance and intelligence of the agents with various heuristics gathering during the testing and pilot phase of the project.
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Service Task Categories Collaboration task
requires more than one workforce members or even or a workforce plus the user
meeting scheduling and negotiation On-site task requires
traveling of the workforce member(s) to a specific location routing and scheduling support
Personal task vs Flexible task Personal task requires one or more specific member(s) Flexible task allows capability matching for the best possible
candidate(s) Remote task
requires communications support connected to Enterprise Information System (EIS) Information transcoding or even process adaptation may be
required
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Workforces Service Process Lifecycle
Task Formulation Phase - the creation of a task request and its specification from various sources inside and outside the enterprise.
The Matchmaking Phase - the tactical identification of the possible workforce capable of the task and rank a subset of them for consideration in the Brokering Phase.
The Brokering Phase - the negotiation with a shortlist of workforce to pick the best available one for a suitable appointment time according to their schedule, location, and preferences.
The Commuting Phase - the travel of the workforce (if necessary), their vehicles (if any), and their locations.
The Service Phase - the actual execution of the task and the necessary support for remote workforce.
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MAIS Overview for MWM
Report Diagnosis Agent
User Agent Cluster
Enterprise Information System
Enterprise Knowledge
Base
Workforce Agent Cluster
Service Support Agent Cluster
Matchmaking Agent Cluster
Broker Agent Cluster
Task Request
Location Database
CommutingAgent Cluster
Task FormulationAgent Cluster
CallCenter
Task Request
Portal
Task Request
Task Request
CapabilityInformation
Task Validation Agents
Request Translation Agents
ValidatedRequest
Capability Analysis Agents
Cost Evaluation Agents
Alert AgentsAppointment Agents
Alerts
ShortlistAppointment
Locator AgentsVehicle Agents
Route Advisory Agents
Negotiation
Location
WorkforceInformation
Location
Collaboration Session AgentsRemote EIS Agents
Monitor Agents
Service Support
EIS Interactions
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Evaluations – Users’ Perspective
Assist their work Workforce tends to become mobile Provision of anytime and anywhere connections
to EIS, colleagues, and clients Agent based adaptation and personalization Agent automation helps reduce tedious
collaboration tasks (e.g., meeting scheduling and structured negotiation)
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Management’s Perspective
Costs vs. Benefits Justified if MWM system helps improve productivities Locating mobile workforce members Location dependent job allocation / scheduling Improve communications (staff and clients) Improve customer relationships; indirectly, business
opportunities MAIS infrastructure integrates disparate heterogeneous
organizational applications Agents help improve the quality and consistency of
decision results through pre-programmed intelligence Adaptation / integration of existing systems by wrapping
them with communication and information agents => cost effective + development time
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System Developer’s Perspective
System development costs and subsequent maintenance efforts
Our methodology helps systematic fine-grained requirements elicitation of the functions of various agent types
Loosely coupled and tightly coherent intelligent software modules encapsulated in agents => manage system complexity
Agents are highly reusable and adaptable Shorten the system development time via
adaptation and integration Keep up with fast evolving technologies
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Conclusion A pragmatic approach of developing a MWM system
with an MAIS infrastructure Meta-model of MAIS and a layer infrastructure
framework Multiple platforms (in particular wireless mobile ones)
and their integration with the EIS Overview of MVM requirements and process lifecycle Methodology for analysis and design of a MAIS for MWM Discuss the design of each agent cluster corresponding
to each phase of the MWM process lifecycle Merits and applicability of our approach from the
perspectives of major system stakeholders
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Further and Ongoing work Only after tasks management for mobile
workforces have been adequately studied, the problem of managing a complete mobile workflow can be tackled
Study or re-examine the technical and management perspectives of each phase and functions of the MWM process in details
A reference model for this new MWM application area
Capability based job allocation and scheduling for MWM
MAIS architecture for other emerging domains, e.g., m-tourism, m-government
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Q&A
Thank you!
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