06231 Abstracts Collection -- Towards Affordance-Based Robot Control

From June 5 to June 9, 2006, the Dagstuhl Seminar 06231 ``Towards Affordance-Based Robot Control\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. %The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. Additionally, papers related to a selection of the above-mentioned presentations willbe published in a proceedings volume (Springer LNAI) early in 2007

Context-Independent Task Knowledge for Neurosymbolic Reasoning in Cognitive Robotics

One of the current main goals of artificial intelligence and robotics research is the creation of an artificial assistant which can have flexible, human like behavior, in order to accomplish everyday tasks. A lot of what is context-independent task knowledge to the human is what enables this flexibility at multiple levels of cognition. In this scope the author analyzes how to acquire, represent and disambiguate symbolic knowledge representing context-independent task knowledge, abstracted from multiple instances: this thesis elaborates the incurred problems, implementation constraints, current state-of-the-art practices and ultimately the solutions newly introduced in this scope. The author specifically discusses acquisition of context-independent task knowledge from large amounts of human-written texts and their reusability in the robotics domain; the acquisition of knowledge on human musculoskeletal dependencies constraining motion which allows a better higher level representation of observed trajectories; the means of verbalization of partial contextual and instruction knowledge, increasing interaction possibilities with the human as well as contextual adaptation. All the aforementioned points are supported by evaluation in heterogeneous setups, to bring a view on how to make optimal use of statistical & symbolic applications (i.e. neurosymbolic reasoning) in cognitive robotics. This work has been performed to enable context-adaptable artificial assistants, by bringing together knowledge on what is usually regarded as context-independent task knowledge

Modeling Memes: A Memetic View of Affordance Learning

This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura\u27s Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de ComputadoresThis dissertation presents a system for haptic interaction and self-supervised learning mechanisms to ascertain navigation affordances from depth cues. A simple pan-tilt telescopic arm and a structured light sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback. The system aims at incrementally develop the ability to assess the cost of navigating in natural environments. For this purpose the robot learns a mapping between the appearance of objects, given sensory data provided by the sensor, and their bendability, perceived by the pan-tilt telescopic arm. The object descriptor, representing the object in memory and used for comparisons with other objects, is rich for a robust comparison and simple enough to allow for fast computations. The output of the memory learning mechanism allied with the haptic interaction point evaluation prioritize interaction points to increase the confidence on the interaction and correctly identifying obstacles, reducing the risk of the robot getting stuck or damaged. If the system concludes that the object is traversable, the environment change detection system allows the robot to overcome it. A set of field trials show the ability of the robot to progressively learn which elements of environment are traversable

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

Message Deletion on Telegram: Affected Data Types and Implications for Computational Analysis

Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Teknologian mahdollistamien kognitiivisten affordanssien mittaaminen : mittarin kehittäminen havainnointityökalusta kyselymittariksi

Tavoitteet. Tämän tutkielman tavoitteena on kehittää Cognitive Affordances of Technologies Scale (CATS) -mittaria. Mittarin tarkoituksena on löytää oppimisen kognitiivisia affordansseja erilaisista teknologiaa hyödyntävistäoppimisympäristöistä. Sitä käytetään koulutuksen kehit-tämiseen. Tässä tutkielmassaei vertailla eri ryhmiä, vaan kuvataan, millaisia tarjoumia erilai-set oppimisympäristöt tuottavat oppijoille. Teoreettinen viitekehys. Kognitiiviset affordanssit ovat ympäristön tarjoamia mahdollisuuksia eli tarjoumia, joita jokainen tulkitsee omasta perspektiivistään. Sulautetun oppimisen (blended learning) ympäristöt sekä virtuaalitodellisuudetta sisältävät oppimisympäristöt kuuluvat teknologiaa hyödyntäviin ympäristöihin. Aiemmassa tutkimuksessa CATS-mittaria on käytetty ainoastaan havainnointitutkimuksessa, ja se on sisältänyt seitsemän kategoriaa ja 41 kriteeriä. Menetelmät. Neljän eurooppalaisen yliopiston opiskelijat ja yhden suomalaisen yrityksen työntekijät vastasivat muokattuun CATS-kyselyyn. Koko aineistoa (N = 134) hyödynnettiin mittarin kehittämisessä. Mittarin testaamisessa käytettiinmuun muassa faktorianalyysiä. Pääryhmät olivat sulautuvaoppiminen ja virtuaalitodellisuutta sisältävä oppiminen. Tulokset ja johtopäätökset. Uudessa CATS-mittarissa on kuusi kategoriaa ja 27 kriteeriä. Pääryhmien osallistujat kokivat, että eniten tarjoumia tulitutkimuspohjaisen oppimisen ja vuorovaikutuksellisen oppimisen kategorioista, joten sulautetun oppimisen ympäristö tarjosi sa-manlaisia oppimisen kognitiivisia affrodansseja kuin virtuaalitodellisuutta sisältävä oppimisympäristö. Tulos ei ole täysin yllättävä, koska kirjallisuuden perusteella virtuaalitodellisuus voidaan tulkita osaksi sulautettua oppimista. Tulevaisuudessa oppimisympäristöjen tutkiminen affordansseittain voi selkeyttää eri teknologioiden rooleja oppimisympäristöissä.Purpose. The aim of this thesisis the development of the Cognitive Affordances of Technologies Scale (CATS) instrument. The purpose of the instrument is to is to map different cognitive affordances of learning indifferent technology-enhanced learning environments. The instrument is used to developand improveeducationand learning modules.In this thesis, dif-ferent groups are not compared, but it is explored what different learning environments offer for learning. Theoretical framework. Cognitive affordances are offerings in the environment that everyone interprets from their own perspective. Technology-enhanced environments include blended learning environments and Virtual Reality (VR)-enhanced learning environments. In a previous study building on the CATS instrument, the instrument was only used in an observational study and it contained seven categories and 41 items. Methods. Students from four European universities and employees of one Finnish company filled inthe modified CATS survey. Data collected from all participants (N = 134) were used in the development of the instrument. In testing the instrument, e.g., factor analysiswas applied. The main groups were blended learning and VR-enhanced learning. Findings and conclusions.The new instrument has six categories and 27 items. Participants of the main groups reported having experienced the most affordances in the categories Inquiry-Based Learning and Discourse/Dialogic Learning. Hence, it seems that the blended learning environment afforded similar cognitive affordances of learning as VR-enhanced learning environments. This finding is not entirely surprising, as based on the literature, VR can be interpreted as part of blended learning.In context of educational implementation, exploring the learning environments by affordances could clarify the roles of different technologies in learning environments in future research