Reducing complexity of consumer electronics interfaces using commonsense reasoning

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 94-100).User interfaces to consumer electronics devices - Video recorders, phones, cameras, washing machines, microwave ovens, etc. - are getting too complicated to be easily used by ordinary consumers. We believe that what is responsible for such complication is a design philosophy which simply maps functions the device can perform to controls like buttons and menu items. That leaves the users with the difficult cognitive task of mapping their goals onto the devices' capabilities - a frustrating and error-prone process. Our hypothesis is that we can provide better assistance to the user using Commonsense Reasoning leading to shorter interactions with the devices. Commonsense can infer the users' likely goals from watching their actions, and anticipate what capabilities of the device can fulfill the users' needs. As devices gain networking capabilities and interact with other devices, Commonsense can also help devices cooperate in support of the users' goals.by José Humberto Espinosa Christlieb.S.M

Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology

Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with commerce and society. Today's AI technology, such as expert systems and intelligent assistants, pose some difficult questions of risk, trust and accountability. In this paper, we present these concerns, examining them in the context of historical developments that have shaped the nature and direction of AI research. We also suggest the exploration and further development of two paradigms, human intelligence-machine cooperation, and a sociological view of intelligence, which might help address some of these concerns.Comment: Preprin

Context-aware management of multi-device services in the home

MPhilMore and more functionally complex digital consumer devices are becoming embedded or scattered throughout the home, networked in a piecemeal fashion and supporting more ubiquitous device services. For example, activities such as watching a home video may require video to be streamed throughout the home and for multiple devices to be orchestrated and coordinated, involving multiple user interactions via multiple remote controls. The main aim of this project is to research and develop a service-oriented multidevice framework to support user activities in the home, easing the operation and management of multi-device services though reducing explicit user interaction. To do this, user contexts i.e., when and where a user activity takes place, and device orchestration using pre-defined rules, are being utilised. A service-oriented device framework has been designed in four phases. First, a simple framework is designed to utilise OSGi and UPnP functionality in order to orchestrate simple device operation involving device discovery and device interoperability. Second, the framework is enhanced by adding a dynamic user interface portal to access virtual orchestrated services generated through combining multiple devices. Third the framework supports context-based device interaction and context-based task initiation. Context-aware functionality combines information received from several sources such as from sensors that can sense the physical and user environment, from user-device interaction and from user contexts derived from calendars. Finally, the framework supports a smart home SOA lifecycle using pre-defined rules, a rule engine and workflows

Breaking down brick walls: Design, construction, and prototype fabrication knowledge in architecture

Architectural designs are not just collections of 3D objects. Architects have both high-level aesthetic design intent, and intent for the functionality of the building; these must eventually translate into real-world construction materials and processes. Physical prototypes are still essential for the architect and their clients to get a feel for whether designs "work". An exciting recent development in architecture is the use of industrial robots to automatically construct 3D prototype architectural models. But programming the robots requires tedious procedures of low-level commands, far removed from the designer's intent. Adeon is a system that integrates high-level architectural design knowledge, including aesthetic and stylistic intent, with knowledge about materials and construction processes, and robot programming code for constructing prototype 3D physical models. It centers around collecting and associating "common sense" knowledge, expressed in English and converted to a knowledge representation about the various levels. It provides a graphic editor that allows architects to draw high-level aesthetic designs, perhaps referencing known styles or historical examples, and retrieving relevant construction, materials, and cost information. It automatically produces a robot program for constructing the prototype. We present examples detailing the design of various styles of brick walls. Adeon is an interesting example of how to provide an interface for creative work that spans both high-level and low-level concerns

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–6) IEEE.Ayala, I., Amor, M., & Fuentes, L. (2013). Self-configuring agents for ambient assisted living applications. Personal and Ubiquitous Computing, 17(6), 1159–1169.Cetina, C., Giner, P., Fons, J., & Pelechano, V. (2009). Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer, 42(10), 37–43.Cook, D. J. (2009). Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 51–55.Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). An architecture for requirements-driven self-reconfiguration. In Advanced information systems engineering (pp. pp 246–260). Springer.De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7), 1313–1321.Huhns, M., & et al. (2005). Research directions for service-oriented multiagent systems. IEEE Internet Computing, 9, 69–70.Iftikhar, M. U., & Weyns, D. (2014). Activforms: active formal models for self-adaptation. In SEAMS, (pp 125–134).Kucher, K., & Weyns, D. (2013). A self-adaptive software system to support elderly care. Modern Information Technology, MIT.Lieberman, H., & Espinosa, J. (2006). A goal-oriented interface to consumer electronics using planning and commonsense reasoning. In Proceedings of the 11th international conference on Intelligent user interfaces (pp. 226–233).Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4), 211–226.Loseto, G., Scioscia, F., Ruta, M., & Di Sciascio, E. (2012). Semantic-based smart homes: a multi-agent approach. In 13th Workshop on objects and Agents (WOA 2012) (Vol. 892, pp. 49–55).Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., Paolucci, M., Parsia, B., Payne, T., & et al (2004). OWL-S: Semantic markup for web services. W3C Member Submission, 22, 2007–2004.Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G, & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447–1459.Molina, J. M., Corchado, J. M., & Bajo, J. (2008). Ubiquitous computing for mobile environments. In Issues in multi-agent systems (pp 33–57). Birkhäuser, Basel.Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540–1557. doi: 10.1016/j.jss.2012.01.045 .Rao, A., & Georgeff, M. (1995). BDI agents: From theory to practice. In Proceedings of the first international conference on multi-agent systems (ICMAS95) (pp. 312–319).Reddy, Y. (2006). Pervasive computing: implications, opportunities and challenges for the society. In 1st International symposium on pervasive computing and applications (p. 5).de Silva, L., & Padgham, L. (2005). Planning as needed in BDI systems. International Conference on Automated Planning and Scheduling.Singh, P. (2002). The public acquisition of commonsense knowledge. In Proceedings of AAAI Spring symposium acquiring (and using) linguistic (and world) knowledge for information access

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments