Theory of Robot Communication: II. Befriending a Robot over Time

In building on theories of Computer-Mediated Communication (CMC), Human-Robot Interaction, and Media Psychology (i.e. Theory of Affective Bonding), the current paper proposes an explanation of how over time, people experience the mediated or simulated aspects of the interaction with a social robot. In two simultaneously running loops, a more reflective process is balanced with a more affective process. If human interference is detected behind the machine, Robot-Mediated Communication commences, which basically follows CMC assumptions; if human interference remains undetected, Human-Robot Communication comes into play, holding the robot for an autonomous social actor. The more emotionally aroused a robot user is, the more likely they develop an affective relationship with what actually is a machine. The main contribution of this paper is an integration of Computer-Mediated Communication, Human-Robot Communication, and Media Psychology, outlining a full-blown theory of robot communication connected to friendship formation, accounting for communicative features, modes of processing, as well as psychophysiology.Comment: Hoorn, J. F. (2018). Theory of robot communication: II. Befriending a robot over time. arXiv:cs, 2502572(v1), 1-2

Crossmodal content binding in information-processing architectures

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any of its sensors on its own could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with its sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and other approaches, can be combined to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot

Challenges for an Ontology of Artificial Intelligence

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What “are” these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as “normal,” and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science

A Model-Driven Engineering Approach for ROS using Ontological Semantics

This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A Model-Driven Engineering Approach for ROS using Ontological Semantic

Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape

The unsolvability of the mind-body problem liberates the will

The mind-body problem is analyzed in a physicalist perspective. By combining the concepts of emergence and algorithmic information theory in a thought experiment employing a basic nonlinear process, it is argued that epistemically strongly emergent properties may develop in a physical system. A comparison with the significantly more complex neural network of the brain shows that also consciousness is epistemically emergent in a strong sense. Thus reductionist understanding of consciousness appears not possible; the mind-body problem does not have a reductionist solution. The ontologically emergent character of consciousness is then identified from a combinatorial analysis relating to system limits set by quantum mechanics, implying that consciousness is fundamentally irreducible to low-level phenomena. In the perspective of a modified definition of free will, the character of the physical interactions of the brain's neural system is subsequently studied. As an ontologically open system, it is asserted that its future states are undeterminable in principle. We argue that this leads to freedom of the will

Task planning using physics-based heuristics on manipulation actions

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft

On the Solvability of the Mind-Body Problem

The mind-body problem is analyzed in a physicalist perspective. By combining the concepts of emergence and algorithmic information theory in a thought experiment employing a basic nonlinear process, it is shown that epistemically strongly emergent properties may develop in a physical system. Turning to the significantly more complex neural network of the brain it is subsequently argued that consciousness is epistemically emergent. Thus reductionist understanding of consciousness appears not possible; the mind-body problem does not have a reductionist solution. The ontologically emergent character of consciousness is then identified from a combinatorial analysis relating to universal limits set by quantum mechanics, implying that consciousness is fundamentally irreducible to low-level phenomena

Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system