Ioannis Athanasiadis | Γιάννης Αθανασιάδης
Information Technologies in Environmental Engineering
Filed Under (Journal articles) by I N Athanasiadis on 01-05-2009
In Environmental Modelling and Software, 24(5):577-587, 2009.
Abstract Models, and to a lesser extent datasets, embody sophisticated statements of ecological knowledge. Yet, the knowledge they contain is rarely self-contained enough for them to be understood and used – by humans or machines – without the ecologist’s mediation. This severely limits the options in reusing ecological models and connecting them to dataset or other models. The notion of “declarative modelling” has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations. Semantically-aware Ecological Modelling (SEM) is a way of designing ecological datasets and models based on the independent, standardized formalization of the underlying ecology, resulting from merging the rationale of declarative modelling with the most recent advances in computer science and integrative visions such as the Semantic Web. In this paper, we discuss the present and preview the future of semantic modelling in Ecology: from the semantically-mediated integration approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the more far-fetched but promising “strong” approach where the knowledge is the key to not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery.
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Filed Under (Journal articles) by I N Athanasiadis on 22-04-2009
In Environmental Modelling and Software, 24(12):1491-1500, 2009.
Abstract 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.
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Filed Under (Journal articles) by I N Athanasiadis on 31-03-2009
In Environmental Modelling and Software, 24(11):1264-1273, 2009.
Abstract The Meteorological Service of Cyprus operates a Doppler radar at the mountainous region of the island. Data-streams recorded by the radar are used for weather forecasting and, especially, for identifying oncoming precipitation incidents and issuing (potential) warnings. However, the continuous processing and evaluation of radar data requires significant efforts by the meteorologists, both for data processing, storage, and maintenance, as well as for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus’ agents undertake data-management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. In addition, Abacus’ agents can identify potentially hazardous incidents, disseminate preprocessed information over the web, and enable warning services provided via email notifications. In this paper, Abacus’ agent architecture is detailed and agent communication for information diffusion is discussed. Focus is also given on the fully customizable logical rule-bases used for agent reasoning required in decision-support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.
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Filed Under (Journal articles) by I N Athanasiadis on 22-01-2009
In Environmental Science and Policy, 12(5):573-587, 2009.
Abstract A major bottleneck for data-based policy making is that data sources are collected, managed, and distributed by different institutions, residing in different locations, resulting in conceptual and practical problems. The use of dispersed data for agricultural systems research requires the integration of datasources, which means to ensure consistency in data interpretations, units, spatial and temporal scales, to respect legal regulations of privacy, ownership and copyright, and to enable easy dissemination of data. This paper describes the SEAMLESS integrated database on European agricultural systems. It contains data on cropping patterns, production, farm structural data, soil and climate conditions, current agricultural management and policy information. To arrive at one integrated database, a shared ontology was developed according to a collaborative process, which facilitates interdisciplinary research.The paper details this process, which can be re-used in other research projects for integrating data sources.
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Filed Under (Journal articles) by I N Athanasiadis on 22-04-2008
In International Journal of Metadata, Semantics and Ontologies, 3(3):210-225, 2008.
Abstract We describe collaborative efforts among a group of knowledge representation experts, domain scientists, and scientific information managers in developing knowledge models for ecological and environmental concepts. The development of formal, structured approaches to knowledge representation used by the group (i.e., ontologies) can be informed by evidence marshalled from unstructured approaches to knowledge representation and semantic tagging already in use by the community.
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Filed Under (Journal articles) by I N Athanasiadis on 31-01-2008
In Mathematics and Computers in Simulation, 78(2-3):412-423, 2008.
Abstract 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.
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Filed Under (Journal articles) by I N Athanasiadis on 22-01-2008
In Information Technologies in Environmental Engineering, 1:3-11, 2008.
Abstract Within Seamless project, a set of constituent agricultural simulation and optimization models is required to be integrated for facilitating assessment studies. Each one of the models has been developed by a different research group, according to dissimilar modeling approaches, implementation designs, and programming tools. As a mediator among these heterogeneous constituent peers, we introduce the Seamless Knowledge Manager component for incubating the data exchanged by the models. The Seamless Knowledge Manager has been developed following a novel approach that exploits ontologies and semantic modeling. Specifically, a declarative approach has been utilized for specifying the data exchanged by the models and has been used as the basis for software development and integration. This paper presents in detail the methodology used for developing the Knowledge Manager and two alternative implementations. The architecture is demonstrated for integrating modules generating agricultural management alternatives.
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Filed Under (Journal articles) by I N Athanasiadis on 11-02-2007
In International Journal of Product Lifecycle Management, 2(2):173-186, 2007.
Abstract Software agent technology has matured enough to produce intelligent agents, which can be used to control a large number of concurrent engineering tasks. Multi-agent systems are communities of agents that exchange information and data in the form of messages. The agents’ intelligence can range from rudimentary sensor monitoring and data reporting, to more advanced forms of decision-making and autonomous behavior. The behavior and intelligence of each agent in the community can be obtained by performing data mining on available application data and the respected knowledge domain. We have developed Agent Academy, a software platform for the design, creation, and deployment of multiagent systems, which combines the power of knowledge discovery algorithms with the versatility of agents. Using this platform, we illustrate how agents, equipped with a data-driven inference engine, can be dynamically and continuously trained. We also discuss three prototype multi-agent systems developed with Agent Academy.
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Filed Under (Journal articles) by I N Athanasiadis on 18-01-2007
In Engineering Applications of Artificial Intelligence, 20(8):1097-1111, 2007.
Abstract 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.
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Filed Under (Journal articles) by I N Athanasiadis on 11-12-2006
In Knowledge-based Systems, 20(4):388-396, 2007.
Abstract Data mining has proven a successful gateway for discovering useful knowledge and for enhancing business intelligence in a range of application fields. Incorporating this knowledge into already deployed applications, though, is highly impractical, since it requires reconfigurable software architectures, as well as human expert consulting. In an attempt to overcome this deficiency, we have developed Agent Academy, an integrated development framework that supports both design and control of multi-agent systems (MAS), as well as `agent training’. We define agent training as the automated incorporation of logic structures generated through data mining into the agents of the system. The increased flexibility and cooperation primitives of MAS, augmented with the training and retraining capabilities of Agent Academy, provide a powerful means for the dynamic exploitation of data mining extracted knowledge. In this paper, we present the methodology and tools for agent retraining. Through experimented results with the Agent Academy platform, we demonstrate how the extracted knowledge can be formulated and how retraining can lead to the improvement – in the long run – of agent intelligence.
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Internet for peace
Environmental Modelling & Software
