KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

An integrated fuzzy risk assessment for seaport operations

Seaport operations are characterised by high levels of uncertainty, as a result their risk evaluation is a very challenging task. Much of the available data associated with the system’s operations is uncertain and ambiguous, requiring a flexible yet robust approach of handling both quantitative and qualitative data as well as a means of updating existing information as new data becomes available. Conventional risk modelling approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the system’s risks. This paper proposes a novel fuzzy risk assessment approach to facilitating the treatment of uncertainties in seaport operations and to optimise its performance effectiveness in a systematic manner. The methodology consists of a fuzzy analytical hierarchy process, an evidential reasoning (ER) approach, fuzzy set theory and expected utility. The fuzzy analytical hierarchy process is used to analyse the complex structure of seaport operations and determine the weights of risk factors while ER is used to synthesise them. The methodology provides a robust mathematical framework for collaborative modelling of the system and allows for a step by step analysis of the system in a systematic manner. It is envisaged that the proposed approach could provide managers and infrastructure analysts with a flexible tool to enhance the resilience of the system in a systematic manner

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

Consumer attitude and behaviour towards 'Flandria' quality labelled tomatoes

In recent years, trust in food safety and food quality has decreased as a result of consecutive food crises. Consequently, numerous quality labels signalling credence characteristics have been established. One of these labels is the Belgian Flandria label for fresh fruit and vegetables. Based on a self-administered consumer survey (n=373), this paper addresses questions about consumer attitudes, behaviour and perception towards tomatoes in general, and the Flandria tomato label in particular. Principal component analysis and consumer segmentation are performed. The findings indicate that the Flandria label may have become the new standard for tomatoes and may have lost a major part of its differentiation potential by being positioned "in the middle" and being too intensively used for a wide range of other fruits and vegetables

Design optimisation of complex space systems under epistemic uncertainty

This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model.This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model

Bayesian decision support for complex systems with many distributed experts

Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Network Analysis, Creative System Modelling and Decision Support: The NetSyMoD Approach

This paper presents the NetSyMoD approach – where NetSyMod stands for Network Analysis – Creative System Modelling – Decision Support. It represents the outcome of several years of research at FEEM in the field of natural resources management, environmental evaluation and decision-making, within the Natural Resources Management Research Programme. NetSyMoD is a flexible and comprehensive methodological framework, which uses a suite of support tools, aimed at facilitating the involvement of stakeholders or experts in decision-making processes. The main phases envisaged for the process are: (i) the identification of relevant actors, (ii) the analysis of social networks, (iii) the creative system modelling and modelling of the reality being considered (i.e. the local socio-economic and environmental system), and (iv) the analysis of alternative options available for the management of the specific case (e.g. alternative projects, plans, strategies). The strategies for participation are necessarily context-dependent, and thus not all the NetSyMod phases may be needed in every application. Furthermore, the practical solutions for their implementation may significantly differ from one case to another, depending not only on the context, but also on the available resources (human and financial). The various applications of NetSyMoD have nonetheless in common the same approach for problem analysis and communication within a group of actors, based upon the use of creative thinking techniques, the formalisation of human-environment relationships through the DPSIR framework, and the use of multi-criteria analysis through the mDSS software.Social Network, Integrated Analysis, Participatory Modelling, Decision Support