<p>Hovedfokus i denne hovedfagsoppgaven er å gi en oversiktlig og lettfattelig innføring i data og kommunikasjonsikkerhet. Det er lagt vekt på å holde oppgaven på et såpass lavt teknisk og matematisk nivå at den vil være mulig å forstå for personer uten store datakunnskaper. Oppgaven er organisert på den måten at den starter med en grunnleggende innføring i kryptografi. Dette er nødvendig for å forstå resten av oppgaven. Deretter forklares og vises de mest utbredte sikkerhetsmekanismene innen data og kommunikasjonsikkerhet. Etter det tar jeg for meg en del spesifikke applikasjoner som brukes mye, og hva som kan gjøres for å sikre kommunikasjonen ved å ta i bruk disse. Til slutt i den teoretiske delen går jeg gjennom andre kommunikasjonsmetoder enn ethernet, som datamaskiner som oftest bruker, og hvilke sikkerhetsmekanismer som ligger innebygd i disse samt hva vi som brukere kan gjøre for å øke sikkerheten.For å gjøre sikkerhet mer tilgjengelig for vanlige brukere har jeg i den praktiske delen av oppgaven valgt å gi detaljerte beskrivelser på oppsett av tre vanlige sikkerhetsmekanismer. De tre jeg har valgt å forklare er IPSec, SSL og digitale signaturer. Grunnen til at valget falt på disse er at de på en måte representerer hvert sitt aktuelle felt innen sikkerhet. IPSec benyttes når en vil ha total sikkerhet i kommunikasjonen mellom maskiner, SSL benyttes når en bare vil ha sikkerhet på informasjonsutveksling mellom utvalgte applikasjoner og digitale signaturer tar for seg integriteten til informasjon som sendes over usikre nettverk. Figurene og beskrivelsene vil gjøre de som leser denne oppgaven i stand til å sette dette opp selv på sine egne datamaskiner.</p>

<p>Målet med denne oppgaven er å se om en automatisk kan organisere kunnskapsobjekter tilrettelagt for et tredimensjonalt navigerbart konseptkart. Forutsetningen for å gjøre dette mulig er at kunnskapen er fremstilt i enheter, kalt læringsobjekter. Et læringsobjekt består av en eller flere kunnskapsobjekter knyttet sammen med ett eller flere læringsmål, og ulike læringsaktiviteter. Et kunnskapsobjekt er bygd opp av en eller flere ressurser. Disse ressursene kan være tekst, lyd, bilde, video osv. En ser for seg at kunnskapsobjektene befinner seg i samlinger, repositories. Kunnskapsobjektene skal kunne utvikles av både faglig veileder og lærende, men hovedsakelig tenker en at de skal produseres av profesjonelle aktører. Her er det snakk om en produksjonslinje. Den profesjonelle aktøren står for produksjon og presentasjon av objektene, mens faglig veileder sørger for innhold og pedagogisk tilrettelegging. Til hvert kunnskapsobjekt er det knyttet en tekstlig beskrivelse som danner grunnlag for vektorisering. Det er denne vektoriseringen som gjør det mulig å indeksere kunnskapsobjektene, samt sammenligne dem. Sammenligningen av de ulike objektene danner grunnlag for klyngeanalyse. Klyngeanalysen vil organisere like objekter i grupper slik at hver gruppe deler felles egenskaper, dvs. har en stor grad av likhet. Med denne grupperingen kan man representere objektene visuelt, for eksempel i en tredimensjonal graf. Som en del av denne oppgaven er det utviklet to prototyper for klyngeanalyse. I den ene benyttes det K-means-algoritme, mens i den andre benyttes en agglomerativ hierarkisk algoritme. Dataen fra disse analysene er tenkt brukt i en tredimensjonal representasjon. Prototypen for K-means har en todimensjonal framstilling slik at man får et visst innblikk i hva en visuell framstilling av objektene innebærer. En vil blant annet se av testene at K-means med brukervalgte initielle klyngerepresentanter gir det beste resultatet for klyngeanalysen, og at de tekstlige beskrivelsene av kunnskapsobjektene bør være av størrelse som en vanlig A4-side.</p>

<p>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.</p>

<p>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.</p>

<p>The Semantic Web is an emerging framework for data reuse and sharing. By giving data clear semantics it allows for machine processing of this information. The Semantic Web technologies range from simple meta data to domain models using the Web Ontology Languge (OWL). Much of the semantics of OWL stems from the Knowledge Representation field of Description Logics. Case-Based Reasoning (CBR) uses specific knowledge in the form of cases to solve problems. The Creek system is a Knowledge-Intensive approach to CBR that combines specific knowledge with general domain knowledge. Part of this work is to define a OWL vocabulary for Creek. This will be concepts used to describe case-bases and domain models, and how these concepts are related to each other. Using this vocabulary, a knowledge model from a Creek system can be described in OWL and shared with others on the Semantic Web. We also examines how domain ontologies can be reused and imported into a Creek knowledge model. We propose a design for Creek to operate in the context of the Semantic Web and its distributed knowledge. Part of the design will be tested by implementation.</p>

<p>This thesis describes a connectionist approach to learning and long-term memory consolidation, inspired by empirical studies on the roles of the hippocampus and neocortex in the brain. The existence of complementary learning systems is due to demands posed on our cognitive system because of the nature of our experiences. It has been shown that dual-network architectures utilizing information transfer successfully can avoid the phenomenon of catastrophic forgetting involved in multiple sequence learning. The experiments involves a Reverberated Simple Recurrent Network which is trained on multiple sequences with the memory being reinforced by means of self-generated pseudopatterns. My focus will be on the implications of how differentiated learning speed affects the level of forgetting, without explicit training on the data used to form the existing memory.</p>

<p>The Baldwin effect is the notion that life time adaptation can speed up evolution by 1) identifying good traits and 2) by genetic assimilation inscribing the traits in the population genetically. This thesis investigates the Baldwin effect by giving an introduction to its history, its current status in evolutionary biology and by reviewing some important experiments on the Baldwin effect in artificial life. It is shown that the Baldwin effect is perceived differently in the two fields; in evolutionary biology the phenomenon is surrounded by controversy, while the approach in artificial life seems to be more straight forward. Numerous computer simulations of the Baldwin effect have been conducted, and most report positive findings. I argue that the Baldwin effect has been interpreted differently in the literature, and that a more well-defined approach is needed. An experiment is performed where the effect of learning on evolution is observed in fitness landscapes of different complexity and with different learning costs. It is shown that the choice of operators and parameter settings are important when assessing the Baldwin effect in computer simulations. In particular I find that mutation has an important impact on the Baldwin effect. I argue that today's computer simulations are too abstract to serve as empirical evidence for the Baldwin effect, but that they nevertheless can be valuable indications of the phenomenon in nature. To assure the soundness of experiments on the Baldwin effect, the assumptions and choices made in the implementations need to be clarified and critically discussed. One important aspect is to compare the different experiments and their interpretations in an attempt to assess the coherence between the different simulations.</p>

<p>In the field of palliative care there is a need to create adaptive questionnaires to minimize the patient's "cognitive load" when acquiring data of the patient's subjective experience of pain. A conversational case based reasoning (CCBR) system can be used as a basis for such questionnaires, and dialogue learning as a method for reducing the number of questions asked, without deterioration of the data quality. In this thesis, methods for question ranking, dialogue inferring, and dialogue learning have been reviewed. A case based reasoning framework is introduced and improved, and based on this, a CCBR system with an extension for dialogue learning has been designed and implemented. The result was tested with well known datasets, as well as new data from a survey on patients' experience of pain. Evaluation shows that dialogue learning can be used to reduce the number of questions asked, but also reveals some problems when it comes to automatically evaluation of solutions found using query biased similarity measures.</p>

<p>This Masters thesis gives a thorough description of a study carried out in the Self-Organizing Systems group at the NTNU. Much {AI research in the later years has moved towards increased use of representationless strategies such as simulated neural networks. One technique for creating such networks is to evolve them using simulated Darwinian evolution. This is a powerful technique, but it is often limited by the computer resources available. One way to speed up evolution, is to focus the evolutionary search on a more narrow range of solutions. It is for example possible to favor evolution of a specific ``species'' by initializing the search with a specialized set of genes. A disadvantage of doing this is of course that many other solutions (or ``species'') are disregarded so that good solutions in theory may be lost. It is therefore necessary to find focusing strategies that are generally applicable and (with a high probability) only disregards solutions that are considered unimportant. Three different ways of focusing evolutionary search for cognitive behaviours are merged and evaluated in this thesis: On a macro level, incremental evolution is applied to partition the evolutionary search. On a micro level, specific properties of the chosen neural network model (CTRNNs) are exploited. The two properties are seeding initial populations with center-crossing neural networks and/or bifurcative neurons. The techniques are compared to standard, naive, evolutionary searches by applying them to the evolution of simulated neural networks for the walking and control of a six-legged mobile robot. A problem simple enough to be satisfactorily understood, but complex enough to be a challenge for a traditional evolutionary search.</p>

<p>This papers uses CPPNs to evolve 2D models of an eye. These models are graded by a fitness function award high information retrieval. The papers shows resulting models with intersting properties and which are similar in form to real world eyes.</p>