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  • 2017

  • Ruiz-Sarmiento, Jose-Raul; Galindo, Cipriano; Gonzalez-Jimenez, Javier (2017): Building Multiversal Semantic Maps for Mobile Robot Operation. In: KNOWLEDGE-BASED SYSTEMS 119, S. 257-272. DOI: 10.1016/j.knosys.2016.12.016

    DOI: https://doi.org/10.1016/j.knosys.2016.12.016 

    Abstract: Semantic maps augment metric-topological maps with meta-information, i.e. l semantic knowledge aimed at the planning and execution of high-level robotic tasks. Semantic knowledge typically encodes human-like concepts, like types of objects and rooms, which are connected to sensory data when symbolic representations of percepts from the robot workspace are grounded to those concepts. Such a symbol grounding is usually carried out by algorithms that individually categorize each symbol and provide a crispy outcome – a symbol is either a member of a category or not. Such approach is valid for a variety of tasks, but it fails at: (i) dealing with the uncertainty inherent to the grounding process, and (ii) jointly exploiting the contextual relations among concepts (e.g. microwaves are usually in kitchens). This work provides a solution for probabilistic symbol grounding that overcomes these limitations. Concretely, we rely on Conditional Random Fields (CRFs) to model and exploit contextual relations, and to provide measurements about the uncertainty coming from the possible groundings in the form of beliefs (e.g. an object can be categorized (grounded) as a microwave or as a nightstand with beliefs 0.6 and 0.4, respectively). Our solution is integrated into a novel semantic map representation called Multiversal Semantic Map (MvSmap), which keeps the sets of different groundings, or universes, as instances of ontologies annotated with the obtained beliefs for their posterior exploitation. The suitability of our proposal has been proven with the Robot@Home dataset, a repository that contains challenging multi-modal sensory information gathered by a mobile robot in home environments. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

    Keywords: Algorithms, Bedienung & Handhabung, Conditional random fields, Mobile robots, Ontologies, Robotics, Semantic maps, Semantic Networks, Symbol grounding, uncertainty, uncertainty handling
  • Sarathy, V.; Scheutz, M.; Malle, B. F. (2017) : Learning behavioral norms in uncertain and changing contexts: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom): Debrecen, Hungary: IEEE, S. 000301-000306

    DOI: https://doi.org/10.1109/CogInfoCom.2017.8268261 

    Abstract: Human behavior is often guided by social and moral norms. Robots that enter human societies must therefore behave in norm-conforming ways as well to increase coordination, predictability, and safety in human-robot interactions. However, human norms are context-specific and laced with uncertainty, making the representation, learning, and communication of norms challenging. We provide a formal representation of norms using deontic logic, Dempster-Shafer Theory, and a machine learning algorithm that allows an artificial agent to learn norms under uncertainty from human data. We demonstrate a novel cognitive capability with which an agent can dynamically learn norms while being exposed to distinct contexts, recognizing the unique identity of each context and the norms that apply in it.

    Keywords: Artificial agent, behavioral norms, behavioural sciences computing, Cognition, Conferences, Dempster-Shafer Theory, deontic logic, Ethics, formal logic, Human behavior, human societies, human-robot interaction, human-robot interactions, ieee xplore, inference mechanisms, learning (artificial intelligence), Libraries, machine learning algorithm, Moral & Ethik, moral norms, norm-conforming ways, Robot kinematics, Social Norms, uncertainty, uncertainty handling
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