The UK National Health Service (NHS) provides the opportunity to undertake local socio-technical system design to help staff maximize the opportunities of using mobile technology whilst minimizing the impact of change to existing patient systems. A real-world example from a local NHS socio-technical system is considered, that contains a collection of mobile clinicians and technology which provides home care to patients. The success of the Mobile NHS service has a high dependency upon the social aspects of the solution and draws upon a combination of people, resources, technology and economic events. This chapter considers multiagent system architectures, to model social complexity, and capture system knowledge, and then outlines a prototyping technique as a means of implementing and testing the design model. It concludes that the practice of implementing a prototype ontology provides a valuable step in clarifying meaning and understanding of concepts at the outset.
There is no distinction of meaning so fine as to consist in anything but a possible difference of practice
—Charles Sanders Peirce, How to make our ideas clearer, 1878.Top
Socio-technical systems have arisen in response to the challenge of understanding complex technical systems that are embedded in a human world (Trist, 1981). Multi-agent System (MAS) architectures are used to build complex technical systems using social concepts such as agents and intelligent agents, which often comprise of many autonomous entities that communicate across multiple organisational tiers. Gathering requirements for such systems and accurately implementing and testing them is a challenge.
As computing moves from single node systems into vast multimode networked systems capable of operating autonomously we need software solutions that are capable of operating with some degree of autonomy acting socially in our best interest. Woodridge et al. (2000) describe this as a software environment which is capable of autonomous action to meet design objectives. Such a system is described as an agent. Taking this definition one step further Woodridge, (2001) describes an intelligent agent as being reactive, proactive and exhibiting social behaviours. If an agent can embody reactive, proactive and social characteristics, then it also possesses the necessary characteristics to be able to transact with other similar agents. Agents can then transact to exchange knowledge. Methodologies for MAS development are still evolving and with the rapid expansion of Web Services and the Semantic Web (Berners-Lee, 1999), tools and architectures are now more in demand.
Hill et al. (2006
) identify that whilst many approaches and tools assist various tasks required to develop a Multi-Agent System there still exists a gap between the generation of MAS models and implementation. Hill’s work provides “A Requirements Elicitation Framework for Agent-Oriented Software Engineering”. Hill provides a preliminary design framework using conceptual graphs to show how the Transaction Agent Modelling (TrAM) approach assisted the design of complex community healthcare payment models. Insight gained during the design process is used to enrich and refine the framework in order that detailed ontological specifications can be constructed. Conceptual Graphs (Sowa, 1984) are a system of logic based on Charles Sanders Peirce’s existential graphs. Conceptual graphs are a flexible and extensible method for knowledge representation, they are particularly useful forms of semantic networks, as they also include generalisation hierarchies of types, relations, and complete graphs (de Moor, 2004). A proposed use of conceptual graphs would extend established methods of designing socio-technical systems such as using the Unified Modelling Language (UML). UML can be extended using conceptual graphs to support ontology engineering, conceptual graphs and semantic networks are examples of knowledge representation languages. They have the full power of first order logic and can represent model and higher-order logic, they have a direct translation into natural language. Conceptual Graphs consists of a “formal language” to access knowledge and meaning in both computer and people systems. Transactional Agent Modelling (TrAM) provides a framework employing conceptual graphs for enriching the requirements gathering process for multi-agent systems. For these reasons conceptual graphs are our formalism of choice to model social complexity.
Key Terms in this Chapter
Conceptual Catalog: The theory of conceptual graphs is primarily formal; conceptual analysis determines the content. A Conceptual Catalog shows how the form can be applied to some of the words and concepts use in a domain. This chapter refers to a catalogue of conceptual relations to be used in the Mobile NHS domain.
Ontology: In the context of knowledge sharing, the chapter uses the term ontology to mean a specification of conceptual relations. An ontology is the concepts and relationships that can exist for an agent or a community of agents. The chapter refers to designing ontologies for the purpose of enabling knowledge sharing and re-use.
Conceptual Graphs: Conceptual graphs consist of a formal language to access knowledge and meaning. A conceptual graph is a graph or network of two kinds of nodes, concepts and relations. They have the full power of first-order logic and can represent model and higher-order logic. Conceptual graphs have a direct translation into natural language.
Intelligent Agents: Intelligent agents refer to software agents capable of being reactive, proactive and exhibiting social behaviour. Linking software and social behaviour intelligent agents are used in the chapter to describe multi-agent system architectures.
Local Socio-Technical: Computer technologies that enable or support local social interaction used in the context of combining local health services with the use of computer and mobile technology. Computer and mobile technology used in a social network to provide local access to patient records at their homes in an attempt to improve the quality of home care to patients.
Conceptual Analysis: In the context of this chapter conceptual analysis is the work of a system or enterprise analyst engaged in knowledge engineering. Conceptual analysis gives content to a graph or network of two kinds of nodes, concepts and relations.
Transaction Agent Modelling: Transaction Agent Modelling refers to a framework employing conceptual graphs for enriching the requirements gathering process for multi-agent systems. The chapter uses transaction agent modeling as a formalism of choice to model social complexity.
Complete Chapter List
Brian Whitworth, Aldo de Moor
Brian Whitworth, Aldo de Moor
Prologue: General Socio-Technical Theory
Ann Borda, Jonathan P. Bowen
Ken Eason, José Abdelnour-Nocera
Cleidson R.B. de Souza, David F. Redmiles
Prologue: Socio-Technical Perspectives
Petter Bae Brandtzæg, Jan Heim
Wilson Huang, Shun-Yung Kevin Wang
Elayne W. Coakes, Peter Smith, Dee Alwis
Prologue: Socio-Technical Analysis
Jonas Sjöström, Göran Goldkuhl
Paul J. Bracewell
Mikael Lind, Peter Rittgen
Harry S. Delugach
Dorit Nevo, Brent Furneaux
Prologue: Socio-Technical Design
Anders I. Mørch
Manuel Kolp, Yves Wautelet
Anton Nijholt, Dirk Heylen, Rutger Rienks
Jos Benders, Ronald Batenburg, Paul Hoeken, Roel Schouteten
Mary Allan, David Thorns
Rebecca M. Ellis
Christopher A. Miller
Prologue: Socio-Technical Implementation
Laura Anna Ripamonti, Ines Di Loreto, Dario Maggiorini
Mohamed Ben Ammar, Mahmoud Neji, Adel M. Alimi
Pernilla Qvarfordt, Shumin Zhai
Claire de la Varre, Julie Keane, Matthew J. Irvin, Wallace Hannum
Jeremy Birnholtz, Emilee J. Rader, Daniel B. Horn, Thomas Finholt
Prologue: Socio-Technical Evaluation
John M. Carroll, Mary Beth Rosson, Umer Farooq, Jamika D. Burge
Tanguy Coenen, Wouter Van den Bosch, Veerle Van der Sluys
Olga Kulyk, Betsy van Dijk, Paul van der Vet, Anton Nijholt, Gerrit van der Veer
Janet L. Holland
David Hinds, Ronald M. Lee
Bertram C. Bruce, Andee Rubin, Junghyun An
Prologue: The Future of Socio-Technical Systems
Peter J. Denning
Theresa Dirndorfer Anderson
Laurence Claeys, Johan Criel
Kenneth E. Kendall, Julie E. Kendall