Decision-making in a proximate model framework: How behaviour flexibility is generated by arousal and attention

Animals must make decisions based on limited information and during a limited amount of time. The time spent exploring possibilities and sampling environmental information, means less time spent at actually gathering and securing recourses. A realistic modelling of animals and their behaviour must include organisms that do not make optimal decisions. Instead, they are constrained by local factors like illumination and conspecifics, as well as the animal’s own state like hungry or afraid. The animal’s personality must also be taken into account when discussing decision-making. Relative stabile traits have been observed in many species, and allow us to predict to a certain extent, their behaviour in the future. In the context of coping with stressful situations, behaviour flexibility (or to what degree the animals react to environmental information) seems to be an important trait. In this thesis, I have explored a computer model for decision-making in fish and studied how behaviour flexibility can be generated in the agents. Behaviour flexibility was measured as the fish’ propensity to change their internal state, called the global organismic state (GOS). The study was done by adjusting two parameters. The first of these control the rate at which motivation declines, after first being elevated (e.g. by seeing a predator). The second parameter controls the filtration of irrelevant information, when the agent is highly motivated. These are called arousal dissipation factor (ADF) and attention modulation factor (AMF), respectively. The results show that both factors affects behaviour flexibility in the fish. ADF influences how often the fish re-evaluate their current state, in light of the available information. Fish that reevaluates more often were more likely to change their GOS. Even though the ADF was sufficient to generate variation in flexibility, information filtering (AMF) was required to generate particularly rigid behaviour, i.e. rarely changing their internal state.Dyr må ta avgjørelser basert på begrenset informasjon og under tidspress. All tid som brukes på å utforske muligheter og innhente informasjon, er tid tapt som kunne vært brukt til å tilegne seg resurser. En realistisk modellering av dyr og deres adferd må ta høyde for at dyrene ikke treffer optimale avgjørelser, men er påvirket av lokale faktorer som lysforhold og konkurranse samt dyrenes egen tilstand, som sult og frykt. En annen ting som har betydning for hvilke valg dyr tar i ulike situasjoner er deres personlighet. Relativt stabile trekk har blitt observert i en rekke arter. Et trekk som ser ut til å spille en viktig rolle i dyrenes håndtering av stressende situasjoner er adferds fleksibilitet, eller til hvilken grad de responderer på enderinger i miljøet. I denne oppgaven har jeg tatt for meg en datamodell for beslutningstagning i fisk og studert hvordan vi kan skape variasjon i trekket adferds fleksibilitet hos individene i modellen. Adferds fleksibilitet ble målt etter hvor tilbøyelige fiskene var til å endre sin interne tilstand, eller «global organismic state» (GOS). Undersøkelsen ble gjort ved å justere på to parametere. Den første av disse parameterne kontrollerer hvor raskt motivasjonen synker etter å ha blitt aktivert (f.eks. av å oppdage et rovdyr). Den andre kontrollerer hvor mye informasjon som filtreres bort når fiskene er svært motiverte. Disse kalles henholdsvis «arousal dissipation factor» (ADF) og «attention modulation factor» (AMF). Resultatene viser at både ADF og AMF er med å påvirke adferds fleksibiliteten hos fiskene. ADF påvirker hvor ofte fiskene revurderer sin nåværende tilstand, i lys av den tilgjengelige informasjonen. Fisker som revurderte oftere, var også mer tilbøyelige til å endre sin GOS. Selv om ADF i seg selv var tilstrekkelig for å skape variasjon i fleksibilitet, var filtrering av informasjon (AMF) avgjørende for at fiskene skulle vise spesielt rigid adferd, dvs. sjeldent endre sin indre tilstand.Masteroppgave i biologiBIO399

Using contextual knowledge in interactive fault localization

Tool support for automated fault localization in program debugging is limited because state-of-the-art algorithms often fail to provide efficient help to the user. They usually offer a ranked list of suspicious code elements, but the fault is not guaranteed to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) – which uses code coverage information of test cases and their execution outcomes to calculate the ranks –, the developer has to investigate several locations before finding the faulty code element. Yet, all the knowledge she a priori has or acquires during this process is not reused by the SBFL tool. There are existing approaches in which the developer interacts with the SBFL algorithm by giving feedback on the elements of the prioritized list. We propose a new approach called iFL which extends interactive approaches by exploiting contextual knowledge of the user about the next item in the ranked list (e. g., a statement), with which larger code entities (e. g., a whole function) can be repositioned in their suspiciousness. We implemented a closely related algorithm proposed by Gong et al. , called Talk . First, we evaluated iFL using simulated users, and compared the results to SBFL and Talk . Next, we introduced two types of imperfections in the simulation: user’s knowledge and confidence levels. On SIR and Defects4J, results showed notable improvements in fault localization efficiency, even with strong user imperfections. We then empirically evaluated the effectiveness of the approach with real users in two sets of experiments: a quantitative evaluation of the successfulness of using iFL , and a qualitative evaluation of practical uses of the approach with experienced developers in think-aloud sessions

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System