Affective Motivational Collaboration Theory

Existing computational theories of collaboration explain some of the important concepts underlying collaboration, e.g., the collaborators\u27 commitments and communication. However, the underlying processes required to dynamically maintain the elements of the collaboration structure are largely unexplained. Our main insight is that in many collaborative situations acknowledging or ignoring a collaborator\u27s affective state can facilitate or impede the progress of the collaboration. This implies that collaborative agents need to employ affect-related processes that (1) use the collaboration structure to evaluate the status of the collaboration, and (2) influence the collaboration structure when required. This thesis develops a new affect-driven computational framework to achieve these objectives and thus empower agents to be better collaborators. Contributions of this thesis are: (1) Affective Motivational Collaboration (AMC) theory, which incorporates appraisal processes into SharedPlans theory. (2) New computational appraisal algorithms based on collaboration structure. (3) Algorithms such as goal management, that use the output of appraisal to maintain collaboration structures. (4) Implementation of a computational system based on AMC theory. (5) Evaluation of AMC theory via two user studies to a) validate our appraisal algorithms, and b) investigate the overall functionality of our framework within an end-to-end system with a human and a robot

Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)

"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

A systematic literature review of decision-making and control systems for autonomous and social robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

The morphofunctional approach to emotion modelling in robotics

In this conceptual paper, we discuss two areas of research in robotics, robotic models of emotion and morphofunctional machines, and we explore the scope for potential cross-fertilization between them. We shift the focus in robot models of emotion from information-theoretic aspects of appraisal to the interactive significance of bodily dispositions. Typical emotional phenomena such as arousal and action readiness can be interpreted as morphofunctional processes, and their functionality may be replicated in robotic systems with morphologies that can be modulated for real-time adaptation. We investigate the control requirements for such systems, and present a possible bio-inspired architecture, based on the division of control between neural and endocrine systems in humans and animals. We suggest that emotional epi- sodes can be understood as emergent from the coordination of action control and action-readiness, respectively. This stress on morphology complements existing research on the information-theoretic aspects of emotion