Ioannis N. Athanasiadis

Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation

The fuzzy lattice reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including space R^N. Tunable generalization is possible based on non-linear (sigmoid) positive valuation functions; moreover, the FLR classifier can deal with missing data. Learning is carried out both incrementally and fast by computing disjunctions of join-lattice interval conjunctions, where a join-lattice interval conjunction corresponds to a hyperbox in R^N. Our testbed in this work concerns the problem of estimating ambient ozone concentration from both meteorological and air-pollutant measurements. The results compare favorably with results obtained by C4.5 decision trees, fuzzy-ART as well as back-propagation neural networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature.

A Hybrid Agent-Based Model for Estimating Residential Water Demand

The global effort toward sustainable development has initiated a transition in water management. Water utility companies use water-pricing policies as an instrument for controlling residential water demand. To support policy makers in their decisions, the authors have developed DAWN, a hybrid model for evaluating water-pricing policies. DAWN integrates an agent-based social model for the consumer with conventional econometric models and simulates the residential water demand-supply chain, enabling the evaluation of different scenarios for policy making. An agent community is assigned to behave as water consumers, while econometric and social models are incorporated into them for estimating water consumption. DAWN’s main advantage is that it supports social interaction between consumers, through an influence diffusion mechanism, implemented via inter-agent communication. Parameters affecting water consumption and associated with consumers’ social behavior can be simulated with DAWN. Real-world results of DAWN’s application for the evaluation of five water-pricing policies in Thessaloniki, Greece, are presented.

Semantic links in integrated modelling frameworks

It is commonly accepted that modelling frameworks offer a powerful tool for modellers, researchers and decision makers, since they allow the management, re-use and integration of mathematical models from various disciplines and at different spatial and temporal scales. However, the actual re-usability of models depends on a number of factors such as the accessibility of the source code, the compatibility of different binary platforms, and often it is left to the modellers own discipline and responsibility to structure a complex model in such a way that it is decomposed in smaller re-usable sub-components. What reusable and interchangeable means is also somewhat vague; although several approaches to build modelling frameworks have been developed, little attention has been dedicated to the intrinsic re-usability of components, in particular between different modelling frameworks. In this paper, we focus on how models can be linked together to build complex integrated models. We stress that even if a model component interface is clear and reusable from a software standpoint, this is not a sufficient condition for reusing a component across different integrated modelling frameworks. This reveals the need for adding rich semantics in model interfaces.

An agent‐based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on‐line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi‐agent system for monitoring and assessing air‐quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air‐quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data‐driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real‐world test case is demonstrated.

Defining assessment projects and scenarios for policy support: Use of ontology in Integrated Assessment and Modelling

Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.

A Framework for Constructing Multi-agent Applications and Training Intelligent Agents

As agent-oriented paradigm is reaching a significant level of acceptance by software developers, there is a lack of integrated high-level abstraction tools for the design and development of agent-based applications, In an effort to mitigate this deficiency, we introduce Agent Academy, an integrated development framework, implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. In addition to these characteristics, our framework goes deeper into agents, by implementing a mechanism for embedding rule-based reasoning into them. We call this procedure agent training and it is realized by the application of AI techniques for knowledge discovery on application-specific data, which may be available to the agent developer. In this respect, Agent Academy provides an easy-to-use facility that encourages the substitution of existing, traditionally developed applications by new ones, which follow the agent-orientation paradigm.

Data mining for agent reasoning: A synergy for training intelligent agents

The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent technology can indeed be used in order to solve DM problems. Looking into the opposite direction though, knowledge extracted through DM has not yet been exploited on MASs. The inductive nature of DM imposes logic limitations and hinders the application of the extracted knowledge on such kind of deductive systems. This problem can be overcome, however, when certain conditions are satisfied a priori. In this paper, we present an approach that takes the relevant limitations and considerations into account and provides a gateway on the way DM techniques can be employed in order to augment agent intelligence. This work demonstrates how the extracted knowledge can be used for the formulation initially, and the improvement, in the long run, of agent reasoning.

Ontology for Seamless Integration of Agricultural Data and Models

This paper presents a set of ontologies developed in order to facilitate the integration of a variety of combinatorial, simulation and optimization models related to agriculture. The developed ontologies have been exploited in the software lifecycle, by using them to specify data communication across the models, and with a relational database. The Seamless ontologies provide with definitions for crops and crop products, agricultural feasibility filters, agricultural management, and economic valuation of crop products, and agricultural and environmental policy, which are in principle the main types of data exchanged by the models. Issues related to translating data structures between model programming languages have been successfully tackled by employing annotations in the ontology.

Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology

Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.

A Privacy-Preserving Cloud Computing System for Creating Participatory Noise Maps

Participatory sensing is a crowd-sourcing technique which relies both on active contribution of citizens and on their location and mobility patterns. As such, it is particularly vulnerable to privacy concerns, which may seriously hamper the large-scale adoption of participatory sensing applications. In this paper, we present a privacy-preserving system architecture for participatory sensing contexts which relies on cryptographic techniques and distributed computations in the cloud. Each individual is represented by a personal software agent, which is deployed on one of the popular commercial cloud computing services. The system enables individuals to aggregate and analyse sensor data by performing a collaborative distributed computation among multiple agents. No personal data is disclosed to anyone, including the cloud service providers. The distributed computation proceeds by having agents execute a cryptographic protocol based on a homomorphic encryption scheme in order to aggregate data. We show formally that our architecture is secure in the Honest-But-Curious model both for the users and the cloud providers. Our approach was implemented and validated on top of the NoiseTube system [1], [2], which enables participatory sensing of noise. In particular, we repeated several mapping experiments carried out with NoiseTube, and show that our system is able to produce identical outcomes in a privacy-preserving way. We experimented with real and simulated data, and present a live demo running on a heterogeneous set of commercial cloud providers. The results show that our approach goes beyond a proof-of-concept and can actually be deployed in a real-world setting. To the best of our knowledge this system is the first operational privacy-preserving approach for participatory sensing. While validated in terms of NoiseTube, our approach is useful in any setting where data aggregation can be performed with efficient homomorphic cryptosystems.