Abstract The Fuzzy Lattice Reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including the Euclidean space. 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 Euclidean space. 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 backpropagation neural networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature.

Abstract Every day, consumers are exposed to advertising campaigns that attempt to influence their decisions and affect their behavior. Word-of-mouth communication-the informal channels of daily interactions among friends, relatives, coworkers, neighbors, and acquaintances-plays a much more significant role in how consumer behavior is shaped, fashion is introduced, and product reputation is built. Macrolevel simulations that include this kind of social parameter are usually limited to generalized, often simplistic assumptions. In an effort to represent the phenomenon in a semantically coherent way and model it more realistically, we developed an influence-diffusion mechanism that follows agent-based social simulation primitives. The model is realized as a multiagent software platform, which we call Dawn (for distributed agents for water simulation).

Abstract 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 waterpricing policies in Thessaloniki, Greece, are presented.

Abstract 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.

Abstract Environmental management information systems need to intervene between several data pools – typically in different physical locations and diverse implementations – to fetch relevant environmental information. We developed a service-oriented architecture to meet this challenge: AISLE, our adaptive intelligent service layer for environmental information management, mediates between existing environmental data providers and actual end-user applications that require preprocessed data streams from the sources. AISLE has two major objectives. The first is to extend the capabilities of existing legacy IT systems residing in environmental monitoring centers and institutions, by targeting typical problems that fetter their quality of service through intelligent modules. The second is to provide a loosely coupled, extensible infrastructure for supplying improved information services – including data preprocessing and management,information dissemination, and multiparty data distribution. AISLE’s service-oriented design, three main clusters of services and development details are presented. By interweaving and interpreting data streams, AISLE can bring crucial environmental information to a wider audience.

Abstract 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.

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.