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

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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

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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

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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

*1

1

*

* *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!