Group Support Systems

Group support systems (GSS) referred to, also, as group decision support systems (GDSS) support integrated collaborative computing, combining communication, hardware and software innovative technologies and including smart devices and intelligent agents to enhance team-based decision making and decision quality, and improve group performance and satisfaction in networked distributed physical and virtual environments. They are based on the use of a context-aware filtering process for adapting content delivered to individual and mobile users, identified by context-aware profiles, expressing the users’ preferences for particular context situations involving them [139].

GDSS support and structure group interaction, and facilitate collaborative learning and training using virtual teams, computer-supported collaborative and distributed work including e-group meetings. Of great significance in the design of GDSS is the release of three moderators from consideration: group size, time and proximity, focussing mainly on task type, level of technology and the existence of facilitation through groupware and GSS tools to enhance the group adaptation process in a generic support perspective.

Of great importance in GDSS is the existence of automated group facilitation using in combination the knowledge and model management capabilities of DSS embedded in a wide range of web-based services composing collaborative support systems.

a) Web-Based Collaborative Support Systems

Web-based collaborative support systems are based on cooperative work using web-based multi-agent collaborative computing and internet technologies, to support collaborative decision making processes, involving information and knowledge produced and exchanged between different agents, forming workgroups or teams including virtual teams.

Collaborative decision making processes are supported by Knowledge-Intensive Intelligent Decision Support Systems (KIIDSS), enabling individual agents to carry out, in a distributed environment, collaborative tasks during the three steps of the collaborative system: decision making modelling, knowledge management, and decision problem solving support. They require rigorous evaluation, comparison and selection of solution and decision alternatives, and optimisation from a global perspective translated in terms of quantified and/or qualitative criteria expressing a policy setting scenario. Their development model and framework are

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generic, adaptive and flexible enough to be used differently in a variety of design decision problems, concurrently integrating multiple cooperative knowledge sources and models.

Collaborative support systems focus on decision knowledge modelling and the use of support schemes for their representational capabilities for non-explicit (tacit) knowledge involving learning and skills. They require a robust set of collaborative new web-based tools for use within a network based environment. These tools provide collaborative decision makers with rapid, accurate access to multiple sources of information, coordinating information and intelligence exchange directly between numerous organisational entities and agents. They include integrated smart devices involving intelligent devices and sensors enabling environmental data capture and obtain the synergy of their integrated use in terms of intelligence-gathering capabilities [174].

b) Collaboration Process Modelling and Visualisation Support

Collaboration process modelling and visualisation consists of sharing and applying expertise of domain experts in modelling and solving problems using a modular approach based on web semantic modelling tools. Complex knowledge systems and domains are broken down into modular components to create a collaborative structure of knowledge models, enabling a virtual communication among collaborating agents and computing models. This knowledge modelling process is based on an iterative knowledge visualisation and rework for managing knowledge processes workflows. This management includes supporting complicated scheduling, sequencing, and iteration of knowledge processes.

The development of web based knowledge services is required to invoke existing knowledge models for enhancement through adapting, expanding, customising and improving them by processing different context situations. This process aims at providing a generic model or system creator that can help in the sharing of knowledge amongst different domain experts, and the converting or translating of abstract ideas contained in tacit knowledge into models needed to build knowledge process models. These models are classified in the form of ontologies that are used as communication schemes tools in the web linked modelling environment, to ease and facilitate the interaction between network agents. This interaction is part of the ontologies management that enables the identification of acceptable agent behaviours that validate the effective use of embedded knowledge models to solve decision making problems in the knowledge domain, and also the different steps of the knowledge processes [174].

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c) Collaboration Environment Design Support

Of great significance in the design of collaboration environment design support illustrated in Figure 3.6, is the inter-layer transformation sequence, containing the following three steps of transformation of:

 Information strategy planning to business area analysis layer

 Business area analysis to perform system design layer, and

 Collaborative system design to construction layer.

Figure 3-6: Collaboration environment design process

The collaboration environment design process is based on the continuous iteration of its different steps, subordinated to the validation of the different design levels. The collaboration model based on the context representation and context description is associated to the success control factors and the goals hierarchy. The business process model is associated to detailed work tasks support and agent interaction protocols to a detailed process model and data model. The system design layer structures the process service capabilities, and is associated to the different agent software, supported by the detailed data flows and the database design.

d) Context Description and Representation Support

Context description and representation of context situations is based on the use of object-oriented representation to describe and structure both the user’s physical and collaborative contexts when designing and implementing distributed collaborative systems to support collaborative activities. The user’s physical context includes the concepts of location, device, and application which can involve a web-based service containing intelligent agents

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interacting in a multi-distributed agent system, whereas the user’s collaborative context includes the concepts of group, role, member, calendar, activity, shared object, and process, which are the collaborative elements.

A context description, which is defined as a composition of context elements, represents the concept of context. This composition group of both physical and collaborative elements is illustrated in Figure 3.7.

Figure 3.7: Context description representation

A context situation is represented in this model by an instance of the class Context Description, which is linked by composition to instances of the class Context Elements and its subclasses. A context element subclass is a concept of context as illustrated in the context model shown in Figure 3.8.

Figure 3.8: Context model representation

The number of physical and collaborative elements can vary depending on the organisational settings and the complexity of the collaborative activities. In the context model representing the elements functional association, the elements Member and Group can be associated to the

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Agent and Web based service in an attempt to represent the service composition model, assuming an intelligent agent is an element of a service designed to describe a set of system functionalities, and a collaborative activity involves a set of services.

e) Group Awareness Information Support

Of great importance in organising collaborative activities are the elaboration, availability and representation of relevant information to supply to different agents during their cooperation when invoking their corresponding services, and enhancing the agents’ interaction learning about each other’s behaviour and influences. This information can be relevant to the modification of the service composition conditions that might alter the agent current context situations, preferences and actions. It contains common knowledge made up of individual agent contributions through the evolvement of their different behaviours when reacting to new context changes, or interacting in different contexts [175]

f) User Preferences Support

Profiles are used to represent the agent interests and preferences, and the interaction and cooperation constraints imposed by different collaboration context situations. These elements often vary during the agent’s cooperation when sharing collaborative activities. A model representing these profiles is based on the use of profile-based filtering rules to support the service composition model in matching the agent current context with the service application context, describing for each agent the needed information for each service associated to a context situation and how it is organised.

The profile model is based on a set of profiles associated to a context description and element, depending whether the preferences are application context based or agent related.

Each profile is linked to a set of preferences which might be shared simultaneously by different context applications and agents. An agent preference is described by a content containing an event and a result, and indicating a format and its different characteristics, as illustrated in Figure 3.9, whereas an application profile is a preference request. The preference matching is supported by the preference association process (PP) [176].

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Figure 3.9: Context element profile and preference model.

Intelligent agents forming the multi-agent system composing knowledge-intensive intelligent decision support systems, are associated to different profiles, depending on their different roles during their cooperation constraints imposed by different collaboration context situations. These systems support the dynamic changes of their environment, in accordance with the context situations These changes are inherent to identified hazards in the domain of indoor surveillance and also to any malfunction of the surveillance network and support systems. They can be classified as follows:

 Implications of changes, changes side effects and upstream hidden changes,

 Identified system deviations, and task divergence, delays and conflicts,

 Changes to state variables, context description elements, agents profiles and preferences (content and display), and internal dependencies,

 Knowledge and decision rules,

 Static and dynamic uncertainty, and

 Specification changes and impact of different external conditions

g) Knowledge-Intensive Intelligent Decision Support Systems

Knowledge-Intensive Intelligent Decision Support Systems (KIIDSS) are automated tools in the configuration of distributed multi-agents systems that increase the speed in developing and modifying collaborative decision making processes, and coordinate and update a large amount of knowledge for different network web-enabled application domains. Elaborate decision-making techniques and tools, and design criteria are used to develop and evaluate various alternative decision making processes with a view to increasing design knowledge.

This knowledge is iteratively used to effectively support decision making processes based on

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a variety of computer-based models, to make correct real-time intelligent decisions during process modelling, knowledge modelling and decision support [174].