A Multi-Agent Infrastructure for Mobile Workforce Management in a Service Oriented Enterprise

1. A Multi-Agent Infrastructure for Mobile Workforce Managementin a Service Oriented Enterprise

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

3. 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

4. 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

5. Layered Infrastructure for MWM Mobile Workforce Management MAIS (Multi - Agent Information System) BDI Agent Collaboration Protocol EIS 3-Tier Implementation Architecture - (Interface Tier / Application Tier / Data Tier)

6. Meta-model of an MAIS

7. 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

8. 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.

9. 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.

10. 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

11. 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.

12. MAIS Overview for MWM User Agent Cluster Workforce Agent Cluster Location Database Enterprise Knowledge Base Service Support Alerts Negotiation Alert Agents Location Appointment Agents Task Request Broker Agent Cluster Appointment Shortlist Commuting Agent Cluster Cost Evaluation Agents Validated Request Matchmaking Agent Cluster Vehicle Agents Locator Agents Task Validation Agents Route Advisory Agents Task Formulation Agent Cluster Capability Analysis Agents Collaboration Session Agents Workforce Information Capability Information Request Translation Agents Remote EIS Agents Service Support Agent Cluster Monitor Agents Task Request Task Request Task Request EIS Interactions Call Center Portal Enterprise Information System Location Report Diagnosis Agent

13. 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)

14. 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

15. 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

16. 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

17. 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

18. Q&A Thank you!