Multiagent System for Image Mining

The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases

Veštačka intelegencija u prikupljanju i analizi podataka u policiji

Complex real problems increasingly require intelligent systems that combine knowledge, techniques and methodologies from various sources. Intelligent systems based on artificial intelligence techniques that are associated with the behavior of people can perform the processes of learning, reasoning and solving all kinds of problems. Such systems, which automatically can perform tasks set by the user or other software, today thankfully called intelligent agents. Independent, intelligent agents on the Internet can be very successful to perform some search work on behalf of and for the needs of different users. For efficient collection, manipulation and management of data, such software can be very interesting from the standpoint of intelligent data analysis in many areas the police. Analysis of the data collected by an intelligent agent (a software robot-bot) can be successfully utilized, among many jobs in the police, and in the field of crime and in particular manifestation of cyber­crime, traffic safety, emergencies, etc. To make the collection and analysis of data from criminal activities on the Internet effective, it is necessary to examine the existing artificial intelligence techniques to be used for the conclusion of the intelligent agents. On the other hand, using of methods of artificial intelligence in finding data along with intelligent data analysis (data mining) should be used, which has found wide use in the area of business, economics, mechanics, medicine, genetics, transport etc.Kompleksni realni problemi sve češće zahtevaju inteligentne sisteme koji kombinuju znanje, tehnike i metodologije iz različitih izvora. Inteligentni sistemi bazirani na tehnikama veštačke inteligencije koje asociraju na ponašanje ljudi mogu da obavljaju procese učenja, zaključivanja i rešavanje raznovrsnih problema. Ovakvi sistemi, koji automatski mogu da izvrše zadatke zadate od strane korisnika ili drugih softvera, danas se sreću pod imenom inteligentni agenti. Samostalno, inteligentni agenti na Internetu mogu veoma uspešno da izvode neki pretraživački posao u ime i za potrebe raznih korisnika. Zbog efikasnog sakupljanja, manipulisanja i upravljanja podacima, ovakvi softveri mogu biti veoma interesantni sa stanovišta inteligentne analize podataka u mnogim oblastima policije. Analiza podataka sakupljenih od strane inteligentnog agenta (softverskog robota - bota) može se uspešno iskoristiti, između mnogih poslova u policiji, i na polju kriminala i naročito pojavnog oblika sajber kriminala, bezbednosti saobraćaja, vanrednih situacija itd. Kako bi sakupljanje i analiza podataka iz kriminalnih aktivnosti na Internetu bila efikasna, neophodno je sagledati postojeće tehnike veštačke inteligencije koje se koriste za zaključivanje u inteligentnim agentima. S druge strane, treba iskoristiti metode veštačke inteligencije u pronalaženju podataka pri inteligentnoj analizi podataka (data mining-u) koja je našla široku primenu u oblasti poslovanja preduzeća, ekonomije, mehanike, medicine, genetike, saobraćaja i sl

Ubiquitous Robotics System for Knowledge-based Auto-configuration System for Service Delivery within Smart Home Environments

The future smart home will be enhanced and driven by the recent advance of the Internet of Things (IoT), which advocates the integration of computational devices within an Internet architecture on a global scale [1, 2]. In the IoT paradigm, the smart home will be developed by interconnecting a plethora of smart objects both inside and outside the home environment [3-5]. The recent take-up of these connected devices within home environments is slowly and surely transforming traditional home living environments. Such connected and integrated home environments lead to the concept of the smart home, which has attracted significant research efforts to enhance the functionality of home environments with a wide range of novel services. The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. The trend whereby the development of smart home services is decoupled from that of smart home devices increases the complexity of this task. As such, it is desirable that smart home services are developed and deployed independently, rather than pre-bundled with specific devices, although it must be recognised that this is not always practical. Moreover, systems need to facilitate the deployment process and cope with any changes in the target environment after deployment. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. All such solutions are considered incomplete, as they lack the ability to meet all smart home requirements efficiently. These requirements include the ability to adapt flexibly to new and dynamic home environments without direct user intervention. Fulfilling these requirements would enhance the performance of smart home systems and help to address cost-effectiveness, considering the financial implications of the manual configuration of smart home environments. Current configuration approaches fail to meet one or more of the requirements of smart homes. If one of these approaches meets the flexibility criterion, the configuration is either not executed online without affecting the system or requires direct user intervention. In other words, there is no adequate solution to allow smart home systems to adapt dynamically to changing circumstances, hence to enable the correct interconnections among its components without direct user intervention and the interruption of the whole system. Therefore, it is necessary to develop an efficient, adaptive, agile and flexible system that adapts dynamically to each new requirement of the smart home environment. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Software Robot (Sobot) for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. The research work was conducted under the Distributed Integrated Care Services and Systems (iCARE) project, which was designed to accomplish and deliver integrated distributed ecosystems with a homecare focus. The auto-configuration Sobot which is the focus of this thesis is a key component of the iCARE project. It will become one of the key enabling technologies for generic smart home environments. It has a profound impact on designing and implementing a high quality system. Its main role is to generate a feasible configuration that meets the given requirements using the knowledgebase of the smart home environment as a core component. The knowledgebase plays a pivotal role in helping the Sobot to automatically select the most appropriate resources in a given context-aware system via semantic searching and matching. Ontology as a technique of knowledgebase representation generally helps to design and develop a specific domain. It is also a key technology for the Semantic Web, which enables a common understanding amongst software agents and people, clarifies the domain assumptions and facilitates the reuse and analysis of its knowledge. The main advantages of the Sobot over traditional applications is its awareness of the changing digital and physical environments and its ability to interpret these changes, extract the relevant contextual data and merge any new information or knowledge. The Sobot is capable of creating new or alternative feasible configurations to meet the system’s goal by utilising inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment. Furthermore, the Sobot has the capability to execute the generated reconfiguration plan without interrupting the running of the system. A proof-of-concept testbed has been designed and implemented. The case studies carried out have shown the potential of the proposed approach to achieve flexible and reliable auto-configuration of the smart home system, with promising directions for future research

Multi-agent data mining with negotiation: a study in multi-agent based clustering

Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach

A brief introduction to agent mining

Agent mining is an emerging interdisciplinary area that integrates multiagent systems, data mining and knowledge discovery, machine learning and other relevant areas. It brings new opportunities to tackling issues in relevant fields more efficiently by engaging together the individual technologies. It will also bring about symbiosis and symbionts that combine advantages from the corresponding constituent systems. In this editorial, we briefly introduce the concept of agent mining, the main areas of research, and challenges and opportunities in agent mining. Finally, we give an overview of the papers in this special issue