Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

Towards augmenting dialogue strategy management with multimodal sub-symbolic context

Abstract. A synthetic agent requires the coordinated use of multiple sensory and effector modalities in order to achieve a social human-robot interaction (HRI). While systems in which such a concatenation of multiple modalities exist, the issue of information coordination across modalities to identify relevant context information remains problematic. A system-wide information formalism is typically used to address the issue, which requires a re-encoding of all information into the system ontology. We propose a general approach to this information coordination issue, focussing particularly on a potential application to a dialogue strategy learning and selection system embedded within a wider architecture for social HRI. Rather than making use of a common system ontology, we rather emphasise a sub-symbolic association-driven architecture which has the capacity to influence the ‘internal ’ processing of all individual system modalities, without requiring the explicit processing or interpretation of modality-specific information

Natural and Artificial Systems: Compare, Model or Engineer?

Some areas of biological research use artificial means to explore the natural world. But how the natural and artificial are related across wide-ranging research areas is not always clear. Relations differ further for bioengineering fields. We propose a taxonomy which would serve to elucidate distinct relations; there are three ways in which the natural is linked to the artificial, corresponding with distinct methods of investigation: i) a comparative approach (natural vs artificial) in which artificial systems are treated in the same way as natural systems, ii) a modeling approach (natural via artificial) in which we use artificial systems to learn about features of natural ones, and iii) an engineering approach (natural pro artificial) in which natural systems are used to draw inspiration for artefacts. Ambiguities about and between these approaches limit the development of fields and impact negatively on interdisciplinary communication

Master of Science

thesisThis study experimentally tested the effects of concurrent tic suppression on a verbal learning task in children with a chronic tic disorder in a semirandomized agematched between groups design using reinforced suppression and tic freely groups. Analyses revealed equal initial learning and immediate recall of words between groups, but the suppression group was able to recall fewer words relative to the control group following a delay while concurrently suppressing. Following a release from suppression and long-delay period, the suppression group again freely recalled an equal number of words but recognized fewer words when presented with a list of words. Despite statistically equal performance between groups at some time points of the task, all means for the suppression group were less than that of the control group. Taken together, these results suggest that tic suppression interferes with registration of newly learned verbal information in long-term memory as well as retrieval of said information while suppressing. Further data collection may reveal that tic suppression results in more broad impairment across learning constructs (i.e., working memory, encoding, registration). This study has implications for people with tic disorders and behavioral treatments of tic disorders

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases

A perspective on lifelong open-ended learning autonomy for robotics through cognitive architectures

[Abstract]: This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address and how they implement them. Among the main functionalities that are taken as relevant for lifelong open-ended learning autonomy are the fact that architectures must contemplate learning, and the availability of contextual memory systems, motivations or attention. Additionally, we try to establish which of them were actually applied to real robot scenarios. It transpires that in their current form, none of them are completely ready to address this challenge, but some of them do provide some indications on the paths to follow in some of the aspects they contemplate. It can be gleaned that for lifelong open-ended learning autonomy, motivational systems that allow finding domain-dependent goals from general internal drives, contextual long-term memory systems that all allow for associative learning and retrieval of knowledge, and robust learning systems would be the main components required. Nevertheless, other components, such as attention mechanisms or representation management systems, would greatly facilitate operation in complex domains.Ministerio de Ciencia e Innovación; PID2021-126220OB-I00Xunta de Galicia; EDC431C-2021/39Consellería de Cultura, Educación, Formación Profesional e Universidades; ED431G 2019/0

The Role of Temporal Distraction on Short-Term Memory and Delayed Recognition

Memory is a complex process that requires the translation of information from an external sensory experience into an internal representation. Once information has been translated into memory, there is little agreement regarding the cognitive structure of memory storage and maintenance. Baddeley (1966) developed a model based on a multi-storage structure which suggested that as information entered through the sensory system, it was relayed by a cognitive control center and placed into storage units based on information type (i.e. auditory, visual, etc.). Baddeley’s (1966) multi-store memory model hypothesized that content translated into memory by two phases: short-term and long-term memory. More recent research supports a unitary model that better accounts for the translation of information from short term memory (STM) to long term memory (LTM) (Jost et al., 2012; Jonides et al., 2008). However, there is still uncertainty of a unitary memory model due to disagreement of the role of distractions during memory translation. The impact of distraction on this process is largely unknown. Understanding the role of distraction during STM encoding and how it affects the formation of LTM can potentially inform treatment for impaired memory. We explored the impact of temporal distractions on short-term memory and delayed recognition for visual content within a modified behavioral task based on Sternberg’s recognition task. Results indicated a negative impact of distractors on memory translation. Implications for future research were discuss to include clinical populations