Lokasjon som kontekstinformasjon blir benyttet stadig mer. Den har vist sin nytteverdi gjennom den mengde av teknologier og applikasjoner, som baserer seg på denne informasjonen. Denne typen applikasjoner er ofte brukersentrert, og gir økt funksjonalitet til en bruker. Presentasjon av en brukers lokasjon, varierer ut fra forskjellige behov og formål. Fordelene og nytteverdien denne informasjonen gir, er i de fleste tilfeller åpenbar. Arbeidet i denne oppgaven ser på hvordan kontekst kan benyttes for å gi assistanse til brukere, med fokus på besøkende og ansatte ved ac{NTNU}, i textit{acl{DIK}}. Hvordan brukermodeller kan benyttes for å gi økt funksjonalitet for brukere av systemet blir utforsket, og et løsningsforslag for hvordan dette kan gjøres blir presentert. Brukermodellene gir et forslag til hvordan informasjon kan innhentes og presenteres for brukere, ut fra hvilke interesser de har. Arbeidet har basert seg på agentrammeverket ac{JADE}, og design av systemet blir gjort ved å følge en ny metode for utvikling av multi-agentsystem. Design av et system blir presentert, hvor alle funksjoner i systemet er basert på agenter, og interaksjonen mellom disse. Arbeidet avsluttet med en prototyp av det designede systemet, og en demonstrasjon av systemet viser hvordan det kan benyttes.

Evolutionary algorithms (EAs) are a class of population-based stochastic search algorithms which have proven themselves to be powerful tools in optimization problems where the search space is complex, contains many local optima, and is so large that an exhaustive search is not possible. An application area where EAs have great potential is in the design of electronic circuits. However, for this type of task such a large representation is typically required for each of the proposed solutions that using an EA approach is not feasible because of the immense computational power this would require. This limitation of EAs is known as the scalability problem: EAs perform well when dealing with problems requiring a small solution representation, but when the required size for these representations increases the EAs quickly become too computationally expensive to be useful. Numerous approaches for dealing with the scalability problem have been proposed. One of the more promising approaches is inspired by the way nature copes with scaling: the process of an organism growing from a single fertilized cell and into a multi-cellular being, called development. By adapting some of the mechanisms of development to a computer program, the EA can evolve a relatively small genome which when developed i.e. decompressed, using this program will represent a solution. There are, however, some problems regarding this approach. One issue is that biological development is such a complex process that implementing it in all its detail is neither feasible nor desired, meaning a decision regarding which mechanisms to implement and which ones to leave out must be made. Another issue is the increased difficulty to evolve a good solution. This occurs because EAs depend on a gradual refinement of the solution to be effective, but with this approach a small change in the genome may lead to a large change in the corresponding solution. This is because in this approach there is no longer a direct correspondence between the genotype space and the solution space, so that what is adjacent in the genotype space may be far apart in the solution space. This means that even though gradual refinement is achieved in genotype space, the changes in the corresponding solution space may appear to be more or less random A novel artificial development system, designed and implemented from scratch, is presented in this thesis. A novel system was built because, although a number of other such system already have been implemented, they are all in the experimental stage, and this system is though to be a useful supplement to the existing ones, providing more material to base the understanding of what may be useful in an artificial development system on. An explorative approach was taken where the implemented system was put through a number of tests to investigate its capabilities. First the systems ability to develop a varied set of different shapes was investigated. Secondly, four parameters were tested for their effect on the system's ability to develop good solutions: the initial number of neighbours, the number of chemical types used (both part of a precondition), the number of cell types available to the system, and the degree of symmetry in the target shapes. The experiments performed showed that the system is able to develop a number of shapes. For the four investigated parameters, indications were found that each has a profound effect on the systems ability to develop a given target.

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Modern search engines have several data sources available to users, e.g. Newssearch, Image search and Video search. When a user enters a query in a searchengine, it is up to the user to choose a different source than the normal web search.On average, a user will only consider the first few occurrences in a search result anddo so in a few seconds. It would therefore be beneficial to the user experienceif the user did not have to limit the sources manually to refine a search.This project will evaluate different machine learning methods to classify relevantsources to a query. The goal of this is having an automated learning system thattakes some labeled input and uses this to help inform or direct the user to therelevant source.The project will take advantage of a Yahoo! product; Yahoo! Query LinguistAnalysis Service (abbreviated QLAS from now on and through the document). Thegoal is to incorporate semantic data from QLAS into the learning system. Thisshould augment the amount of information available to the learning system, andimprove its performance. It is not clear how this semantic data could be combinedwith the training data and incorporated in the learning system. A substantial partof the project will be to explore this.This project was done in cooperation with Yahoo! Technologies Norway AS (YTN).YTN develops Vespa, a search engine platform that has the possibility to searchfrom multiple sources. YTN is interested in researching the field of learning sourcerelevance to improve the search experience in Yahoo services. YTN is also interestedin researching ways data from QLAS could be used by Vespa to enable sourcerelevance classification when Vespa is used in a multiple-index setup.

CLIR, Cross-Lingual Information Retrieval, is a field of research that can behighly useful in web search and for several other applications. Extensiveresearch has been done on possible CLIR implementations, but as of yet thereare no open source frameworks or applications readily available. The thesisfocuses on building such a framework and evaluating it for use on theNorwegian/Spanish language pair.The framework implemented uses query translation to submit queries to existinginformation retrieval (IR) implementations, and the framework itself holds nolow-level IR algorithms. Experiments were performed on a small parallel corpusof Norwegian and Spanish texts, using the Xapian and PostgreSQL IRimplementations. A comprehensive comparison of possible configurations wasdone, and certain measures were shown to be effective when searching fordocuments in either language.The framework is implemented in a modular architecture, allowing the suggestedadditions and amendments to be implemented as add-on components. This is themain intent of the framework, and eases the process of building support foradditional languages as well. For easing the adoption of the framework,additional components and data may be beneficial.Some improvements are also possible for the tested language pair, throughobtaining larger data sets or implementing certain language specificalgorithms. Of particular interest is implementing effective decompounding ofNorwegian compound words and phrase translation support. Suggestions are alsomade for how the system can be used to perform CLIR tasks in other languages.

Modularity is an architectural trait that is prominent in biological neural networks, but strangely absent in evolved artificial neural networks. This report contains the results of a theoretical study focusing on two questions about modularity in neural network systems. How does modularity emerge in biological neural networks, and when could modularity be useful in artificial neural networks?The theoretical study resulted in a hypothesis that modularity in biological neural networks is the result of physical constraints on their architectures. Because these physical constraints affect the digital environments in a different way, modularity does not emerge naturally during evolution of neural networks in a digital medium. Secondly, it is hypothesised that modularity in artificial neural networks can reduce the amount of spatial interference during learning. A phenomenon that is here shown to occur when two outputs that exhibit low correlation are solved using the same neural network structures.Experiments have been performed in order to verify if there are advantages to having modular topologies in order to limit the detrimental effects of spatial interference occurring during learning in artificial neural networks.

Mobil veiledning er et pilotprosjekt ved NTNU som ble kjørt våren 2005 i fagene Operativsystemer( IT2202) og Programmering, videregående kurs (IT1104). Mobil veiledning innebærer at veiledning fra studentassistent foregår over internett i stedet for tradisjonell veiledning på datasal. I mobil veiledning sitter studenter og studentassistenter distribuert og kommuniserer via et videokonferansesystem som tilbyr overføring av lyd, bilde og applikasjonsdeling. Studentene kan dermed delta på veiledningen bare de har en datamaskin med nødvendig programvare installert, internettilgang (bredbånd), webkamera og headset. Bakgrunnen for pilotprosjektet var å teste ut mobil veiledning som arena for veiledning. Undersøkelsen har vist at studentene som har deltatt i pilotprosjektet synes mobil veiledning fungerer bra. Selv om ikke alle er av den oppfatning at det kan erstatte tradisjonell veiledning, så mener de fleste at det er et godt supplement til tradisjonell veiledning. Den største styrken ved mobil veiledning er den mobiliteten det tilbyr studentene. Det betyr at studentene kan få veiledning uavhengig av geografisk lokasjon, bare de har tilgang til internett. Den største ulempen med mobil veiledning er de utfordringer teknologien setter for kommunikasjon mellom deltakerne. Teknologien må læres for å brukes og studenten må ha tilgang på nødvendig utstyr. Erfaringer fra pilotprosjektet viser at teknologien ikke alltid fungerer slik den er ment å fungere. Pakketap i overføringen kan gi dårlig kvalitet på audio/video. Det er bare de mest motiverte studentene som har benyttet seg av mobil veiledning. Det viser seg at organiseringen har konsekvenser for oppslutningen. Utfordringen er å øke oppslutningen. Vi har foreslått flere tiltak. Noen av disse er bedret opplæring i mobil veiledning ved gjennomføring av workshop, og å øke nytteverdien ved å tilby mobil veiledning i flere fag. Mobil veiledning kan gi muligheter som ikke er mulig i en tradisjonell øvingstime. Bruk av privat kommunikasjon muliggjør flere samtidige samtaler i samme rom uten at de forstyrrer hverandre. Undersøkelsen viser også at bruk av privat kommunikasjon gjør studentene mindre nervøse for å benytte systemet til å stille faglige spørsmål til studentassistent. Samtidig ønsker studentene å få med seg spørsmål andre studenter stiller studentassistent. Vi tror derfor det kan være aktuelt å utvikle og gi brukerne opplæring i en egen etikette for hvordan en bør benytte mobil veiledning, slik at brukergruppen som helhet skal få mest mulig utbytte av mobil veiledning. Blant annet å bruke global kommunikasjon mot studentassistent, og tale hvis ikke skriftlig kommunikasjon er mest hensiktsmessig. I følge Moores prinsipper er de studentene som har valgt å delta på pilot prosjektet med i grupperingen innovators og early developers. Bøygen er å komme over grøfta til early majority der teknologien er akseptert av en stor del av den potensielle brukergruppen. Når teknologi har entret dette stadiet, vil utviklingen gå av seg selv, der nye studenter får informasjon og interesse for konseptet fra studenter som allerede benytter verktøyet. Målsetningen med pilotprosjektet er å oppnå Mantovanis konseptuelle modell av sosial kontekst og min hypotese er at forutsetningen for å vurdere pilotprosjektet mot Mantovanis modell er MTO perspektivet. I mobil veiledningsprosjektet var organiseringen mangelfull og som et resultat er det ikke forventet at artifaktet ble forplantet opp i strukturen og dermed heller ikke en del av den sosiale konteksten til enten studentene eller NTNU. Hypotesen om at organisering må endres for å utnytte det fulle potensialet av et nytt artifakt ble bekreftet gjennom Part Two.

It is interesting how we can take a train of thought and transfer this into another person's mind by pushing the air around us. Human language, this complexmedium that distinctly separates humans from animals, has baffled scientists forcenturies. But as it lacks of historical data, researchers wish to benefit fromcomputer science and the field of artificial life to understand the origin oflanguage. This thesis illuminates the potential for using agent-based models toinvestigate the relationship between biology, culture and behavior on anindividual level.This is done in two parts. First, different theories and computational modelsexperimenting with language evolution are presented. This includes a thoroughimplementation of and elaborations on one recent paper, where languageacquisition is illustrated favorable over multiple evolutionary time scales inan agent-based model. In the second part, a more bio-inspired methodology isproposed to make the former model more robust and better suited for extensions.This is demonstrated by letting the agents evolving some social biases, whilethey are conducting a naming game in a social structure. A naming game is anabstraction, often used in the research field, to model the spreading anddiversity of language. Through pair-wise dialogs, the goal of the game is toreach self-organized agreement on naming an arbitrary object in theirenvironment. Given the assumption that communication is beneficial for socialstructure and that social structure is beneficial for reproduction, theexperimental work demonstrates that agents are able to build social structuresthat resembles real life social topologies, although the naming game mighthappen too rapid in respect to the evolving social structure.Hopefully, with support from other disciplines, the presented model is suitedfor further investigation of social, or other functional, traits that caninfluence language evolution.

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