Multicore and FPGA implementations of emotional-based agent architectures

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1307-6.Control architectures based on Emotions are becoming promising solutions for the implementation of future robotic agents. The basic controllers of the architecture are the emotional processes that decide which behaviors of the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, reducing the processing capacity of the main processor to solve complex problems (millions of decisions in a given instant). However, the potential parallelism of the emotional processes permits their execution in parallel on FPGAs or Multicores, thus enabling slack computing in the main processor to tackle more complex dynamic problems. In this paper, an emotional architecture for mobile robotic agents is presented. The workload of the emotional processes is evaluated. Then, the main processor is extended with FPGA co-processors through Ethernet link. The FPGAs will be in charge of the execution of the emotional processes in parallel. Different Stratix FPGAs are compared to analyze their suitability to cope with the proposed mobile robotic agent applications. The applications are set up taking into account different environmental conditions, robot dynamics and emotional states. Moreover, the applications are run also on Multicore processors to compare their performance in relation to the FPGAs. Experimental results show that Stratix IV FPGA increases the performance in about one order of magnitude over the main processor and solves all the considered problems. Quad-Core increases the performance in 3.64 times, allowing to tackle about 89 % of the considered problems. Quad-Core has a lower cost than a Stratix IV, so more adequate solution but not for the most complex application. Stratix III could be applied to solve problems with around the double of the requirements that the main processor could support. Finally, a Dual-Core provides slightly better performance than stratix III and it is relatively cheaper.This work was supported in part under Spanish Grant PAID/2012/325 of "Programa de Apoyo a la Investigacion y Desarrollo. Proyectos multidisciplinares", Universitat Politecnica de Valencia, Spain.Domínguez Montagud, CP.; Hassan Mohamed, H.; Crespo, A.; Albaladejo Meroño, J. (2015). Multicore and FPGA implementations of emotional-based agent architectures. Journal of Supercomputing. 71(2):479-507. https://doi.org/10.1007/s11227-014-1307-6S479507712Malfaz M, Salichs MA (2010) Using MUDs as an experimental platform for testing a decision making system for self-motivated autonomous agents. Artif Intell Simul Behav J 2(1):21–44Damiano L, Cañamero L (2010) Constructing emotions. 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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

Chapter 13 Haptic Creatures

Collaborations between entertainment industries and artificial intelligence researchers in Japan have since the mid-1990s produced a growing interest in modeling affect and emotion for use in mass-produced social robots. Robot producers and marketers reason that such robot companions can provide comfort, healing (iyashi), and intimacy in light of attenuating social bonds and increased socioeconomic stress characteristic of Japanese society since the collapse of the country’s bubble economy in the early 1990s. While many of these robots with so-called “artificial emotional intelligence” are equipped with rudimentary capacities to “read” predefined human emotion through such mechanisms as facial expression recognition, a new category of companion robots are more experimental. These robots do not interpret human emotion through affect-sensing software but rather invite human-robot interaction through affectively pleasing forms of haptic feedback. These new robots are called haptic creatures: robot companions designed to deliver a sense of comforting presence through a combination of animated movements and healing touch. Integrating historical analysis with ethnographic interviews with new users of these robots, and focusing in particular on the cat-like cushion robot Qoobo, this chapter argues that while companion robots are designed in part to understand specific human emotions, haptic creatures are created as experimental devices that can generate new and unexpected pleasures of affective care unique to human-robot relationships. It suggests that this distinction is critical for understanding and evaluating how corporations seek to use human-robot affect as a means to deliver care to consumers while also researching and building new markets for profit maximization