RoboCup@Home: commanding a service robot by natural language.

It was in the ancient Greece that myths were written and, among already there one could nd the human desire of robotic servants. It was Hephaestus, god of technology, blacksmiths, craftsmen and artisans who is said to have built robots to help him on his workshop. This show how deep in our thoughts was this desire that one could nd stories and tales of human-shaped machines that danced in china or inanimate materials like mud that gave shape to golems in Jewish tradition. In the renaissance, a lot of automata began to arise, beginning by Leonardo Da Vinci to the artisans from China and Japan, mankind was trying to produce automatic machines, sometimes for their own bene t, some other times to their delight and fascination. But it wasn't until the digital era that the dream began to seem feasible. After millennia of wondering of automated robots, computers showed that automatic calculus was possible and from this, ideas of an automated mind arose. Theories for cognitive architectures are born since the early stages of arti cial intelligence, cognitive architectures that now are a reality. Thanks to the technological advances and the knowledge about the mind, what once was material for ctional tales, now is feasible and only matter of time. There is a lot of research on robotics and cognition that is beginning to get coupled into what are called "service robots". In this thesis, I present a system that participates in a competition designed for this kind of robots. A competition that have on its basis the same dream that humans have had all around the world for centuries: the cohabitation of humans and service automatons

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Chess Practice and Executive Functioning in a Post-Secondary Student Diagnosed with ADHD: A Single Case Study

This single-case study explored how the influence of chess practice on working memory and other executive functions was perceived by an adult diagnosed with attention deficit hyperactivity disorder (ADHD). Cognitive science has used chess in the study of memory, concentration, attention and expertise (Charness, 1992; Gobet, 1998). The game of chess has also been used in clinical and educational contexts both to enhance cognitive abilities and to change academic outcomes (Hong, 2005). The chess program I designed consisted of a weekly, one hour chess practice for ten weeks during which the participant solved chess puzzles. The selected participant underwent a semi-structured interview pre- and post- the chess intervention and answered the Barkley Adult ADHD Rating Scale (BAARS-IV) and the Barkley Deficit in Executive Function Scale (BDEFS) at the beginning and end of the chess program. Furthermore, the participant answered opened-ended questions about her perceptions of the effects of the chess program after each of eight training sessions. Thematic analysis was performed in an inductive search for general descriptors within the data. The chess training intervention resulted in the participant’s perception of an overall decrease in ADHD symptoms, especially inattentiveness, and improvement in working memory and other executive functions. Implications for further research and practice are identified

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

Deep Lake: a Lakehouse for Deep Learning

Traditional data lakes provide critical data infrastructure for analytical workloads by enabling time travel, running SQL queries, ingesting data with ACID transactions, and visualizing petabyte-scale datasets on cloud storage. They allow organizations to break down data silos, unlock data-driven decision-making, improve operational efficiency, and reduce costs. However, as deep learning takes over common analytical workflows, traditional data lakes become less useful for applications such as natural language processing (NLP), audio processing, computer vision, and applications involving non-tabular datasets. This paper presents Deep Lake, an open-source lakehouse for deep learning applications developed at Activeloop. Deep Lake maintains the benefits of a vanilla data lake with one key difference: it stores complex data, such as images, videos, annotations, as well as tabular data, in the form of tensors and rapidly streams the data over the network to (a) Tensor Query Language, (b) in-browser visualization engine, or (c) deep learning frameworks without sacrificing GPU utilization. Datasets stored in Deep Lake can be accessed from PyTorch, TensorFlow, JAX, and integrate with numerous MLOps tools