Making a robot autonomous has been a common challenge to be overcome since the very beginning. To be an autonomous system, the robot should collect environmental data, interpret them, and act accordingly. In order to accomplish these, some resource management should be conducted. That is, the resources, which are time, and computation power in our case, should be allocated to more important areas. Existing researches and approaches, however, are not always human like. Indeed they don&rsquo / t give enough importance on this. Starting from this point of view, the system proposed in this thesis supplies the resource management trying to be more &rsquo / human like&rsquo / . It directs the focus of attention to where higher resolution algorithms are really needed. This &rsquo / real need&rsquo / is determined by the visual features of the scene, and current importance levels (or weight values) of each of these features. As a further attempt, the proposed system is compared with human subjects&rsquo / characteristics. With unbiased subjects, a set of parameters which resembles a normal human is obtained. Then, in order to see the effect of the guidance, the subjects are asked to concentrate on a single predetermined feature. Finally, an artificial neural network based learning mechanism is added to learn to mimic a single human or a group of humans. The system can be used as a preattentive stage module, or some more feature channels can be introduced for better performance in the future.

This thesis deals with agent behavior evolution for the environment of a real computer game using evolutionary algorithms. The game Unreal Tournament 2004 was chosen, due to its ease of use for creating agents manually with the Pogamut suite of tools. As a decision making structure for the agents yaPOSH reactive plans were used. Due to the demanding needs on the hardware and time a real computer game is not considered to be very suitable for artificial evolution. To overcome this fact a light-weighted environment LightEnv, that simulates only those aspects that are important for agent evolution, was created. The evolution was based on genetic programming modified for use with yaPOSH reactive plans. The evolved agent behavior for death match and team death match game scenarios exceeded the preprogrammed ones and was successfully transferred to Unreal Tournament 2004 environment. In the team death match scenario an interesting behavior that utilizes agent communication was evolved.

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Human-robot interaction (HRI) is the study of how we as humans interact and communicate with robots and one of its subfields is working on how we can improve the collaboration between humans and robots. We need robots that are more user friendly and easier to understand and a key aspect of this is human-like movements and behavior. This project targets a specific set of motions called locomotion and tests them on the humanoid NAO robot. A human-like crawling gait was developed for the NAO robot and compared to the built-in walking gait through three kinds of experiments. The first one to compare the speed of the two gaits, the second one to estimate their sta- bility, and the third to examine how long they can operate by measuring the power consumption and temperatures in the joints. The results showed the robot was significantly slower when crawling compared to walking, and when still the robot was more stable while standing than on all-fours. The power consumption remained essentially the same, but the crawling gait ended up having a shorter operational time due to higher temperature increase in the joints. While the crawling gait has benefits of having a lower profile then the walking gait and could therefore more easily pass under low hanging obsta- cles, it does have major issues that needs to be addressed to become a viable solution. Therefore these are important factors to consider when developing gaits and designing robots, and motives further research to try and solve these problems.

Artificial Intelligence is being developed and implemented into everyday technologies at an ever increasing speed and the need to make Artificial Intelligence behave human-like has shown to be of importance in an effort to reduce the gap between human and computer. This study examines the state of human-like attributes in current the Intelligent Assistant Google Home to then further explore the design opportunities for a more human-like interaction with Intelligent Assistants. Through interviews, design experiments and prototypes this thesis arrives at a final design proposal for future implementation in Intelligent Assistants which defines what a human- like interaction entails for the people involved in this study.

The way characters move and behave in computer and video games are important factors in their believability, which has an impact on the player’s experience. This project explores Imitation Learning using limited amounts of data as an approach to creating human-like AI behaviour in games, and through a user study investigates what factors determine if a character is human-like, when observed through the characters first-person perspective. The idea is to create or shape AI behaviour by recording one's own actions. The implemented framework uses a Nearest Neighbour algorithm with a KD-tree as the policy which maps a state to an action. Results showed that the chosen approach was able to create human-like AI behaviour while respecting the performance constraints of a modern 3D game. / Sättet karaktärer rör sig och beter sig på i dator- och tvspel är viktiga faktoreri deras trovärdighet, som i sin tur har en inverkan på spelarens upplevelse. Det här projektet utforskar Imitation Learning med begränsad mängd data som etttillvägagångssätt för att skapa människolik rörelse för AI-karaktärer i spel, ochutforskar genom en användarstudie vilka faktorer som avgör om en karaktärär människolik, när karaktären observeras genom dess förstapersonsperspektiv. Iden är att skapa eller forma AI-beteende genom att spela in sina egna handlingar. Det implementerade ramverket använder en Nearest Neighbour-algoritmmed ett KD-tree som den policy som kopplar ett tillstånd till en handling. Resultatenvisade att det valda tillvägagångssättet lyckades skapa människolikt AI-beteende samtidigt som det respekterar beräkningskomplexitetsrestriktionersom ett modernt 3D-spel har.

The work presented in this thesis comes in pursuit of the ultimate goal of building spoken and embodied human-like interfaces that are able to interact with humans under human terms. Such interfaces need to employ the subtle, rich and multidimensional signals of communicative and social value that complement the stream of words – signals humans typically use when interacting with each other. The studies presented in the thesis concern facial signals used in spoken communication, and can be divided into two connected groups. The first is targeted towards exploring and verifying models of facial signals that come in synchrony with speech and its intonation. We refer to this as visual-prosody, and as part of visual-prosody, we take prominence as a case study. We show that the use of prosodically relevant gestures in animated faces results in a more expressive and human-like behaviour. We also show that animated faces supported with these gestures result in more intelligible speech which in turn can be used to aid communication, for example in noisy environments. The other group of studies targets facial signals that complement speech. As spoken language is a relatively poor system for the communication of spatial information; since such information is visual in nature. Hence, the use of visual movements of spatial value, such as gaze and head movements, is important for an efficient interaction. The use of such signals is especially important when the interaction between the human and the embodied agent is situated – that is when they share the same physical space, and while this space is taken into account in the interaction. We study the perception, the modelling, and the interaction effects of gaze and head pose in regulating situated and multiparty spoken dialogues in two conditions. The first is the typical case where the animated face is displayed on flat surfaces, and the second where they are displayed on a physical three-dimensional model of a face. The results from the studies show that projecting the animated face onto a face-shaped mask results in an accurate perception of the direction of gaze that is generated by the avatar, and hence can allow for the use of these movements in multiparty spoken dialogue. Driven by these findings, the Furhat back-projected robot head is developed. Furhat employs state-of-the-art facial animation that is projected on a 3D printout of that face, and a neck to allow for head movements. Although the mask in Furhat is static, the fact that the animated face matches the design of the mask results in a physical face that is perceived to “move”. We present studies that show how this technique renders a more intelligible, human-like and expressive face. We further present experiments in which Furhat is used as a tool to investigate properties of facial signals in situated interaction. Furhat is built to study, implement, and verify models of situated and multiparty, multimodal Human-Machine spoken dialogue, a study that requires that the face is physically situated in the interaction environment rather than in a two-dimensional screen. It also has received much interest from several communities, and been showcased at several venues, including a robot exhibition at the London Science Museum. We present an evaluation study of Furhat at the exhibition where it interacted with several thousand persons in a multiparty conversation. The analysis of the data from the setup further shows that Furhat can accurately regulate multiparty interaction using gaze and head movements. / <p>QC 20121123</p>

The main aim of this study, in which 310 university students participated, is to investigate whether or not computer interfaces offering human-like apologetic error messages influence users&rsquo / self-appraisals of performances and actual performances in the computerized environment. For the study, an online instructional material which includes deliberate design problems leading to user frustration was developed. The study is comprised of three phases. In the first phase, based on the CCSARP (Cross-Cultural Study of Speech Act Realization Patterns) coding manual and the studies conducted with the framework provided by the manual, apology strategy sequences were elicited from Turkish participants. Two of these apology strategy sequences were selected for producing two apology error messages. In addition to these apology messages, one plain computer error message was also developed for experimental control. The second phase of the study was conducted to determine whether these three messages were perceived as apologies. It was found out that the two apology messages were perceived as apologies and the plain computer message was not perceived as an apology. In the third phase these three messages were used to investigate the relationship between mood, self-appraisal of performance and actual performance after the transmission of the apologetic error messages. The findings of this study show that the frequencies of apology strategies preferred in the computerized environment are similar with those utilized in the social context. Statistical analyses also reveal that the influence of apology messages on self-appraisal of performance depends on participants&rsquo / mood state and the contents of the apology messages.

Artificial general intelligence is not well defined, but attempts such as the recent listof “Ingredients for building machines that think and learn like humans” are a startingpoint for building a system considered as such [1]. Numenta is attempting to lead thenew era of machine intelligence with their research to re-engineer principles of theneocortex. It is to be explored how the ingredients are in line with the design princi-ples of their algorithms. Inspired by Deep Minds commentary about an autonomy-ingredient, this project created a combination of Numentas Hierarchical TemporalMemory theory and Temporal Difference learning to solve simple tasks defined in abrowser environment. An open source software, based on Numentas intelligent com-puting platform NUPIC and Open AIs framework Universe, was developed to allowfurther research of HTM based agents on customized browser tasks. The analysisand evaluation of the results show that the agent is capable of learning simple tasksand there is potential for generalization inherent to sparse representations. However,they also reveal the infancy of the algorithms, not capable of learning dynamic com-plex problems, and that much future research is needed to explore if they can createscalable solutions towards a more general intelligent system.

Context. Procedural content generation (PCG) refers to algorithmical creation of game content (e.g. levels, maps, characters). Since PCG generators are able to produce huge amounts of game content, it becomes impractical for humans to evaluate them manually. Thus it is desirable to automate the process of evaluation. Objectives. This work presents an automatic method for evaluation of procedural level generators for 2D platform games. The method was used for comparative evaluation of four procedural level generators developed within the research community. Methods. The evaluation method relies on simulation of the human player&apos;s behaviour in a 2D platform game environment. It is made up of three components: (1) a 2D platform game Infinite Mario Bros with levels generated by the compared generators, (2) a human-like bot and (3) quantitative models of player experience. The bot plays the levels and collects the data which are input to the models. The generators are evaluated based on the values output by the models. A method based on the simple moving average (SMA) is suggested for testing if the number of performed simulations is sufficient. Results. The bot played all 6000 evaluated levels in less than ten minutes. The method based on the SMA showed that the number of simulations was sufficiently large. Conclusions. It has been shown that the automatic method is much more efficient than the traditional evaluation made by humans while being consistent with human assessments.