A Cognitive Robotic System for a Human-Following Robot

In this paper we propose a cognitive robotic system that utilizes computational psychology (the Soar cognitive architecture) and an obstacle avoidance method (modified dynamic window approach) in ROS (Robot Operating System) platform for controlling a mobile robot. This system is applied to perform a task of human-following, aiming to help the robot navigate itself to the target person avoiding collision. A cognitive agent based on Soar cognitive architecture is created to reason its current situation and make decisions on movement direction such as go-straight, turn-left or turn-right, whereas the dynamic window approach is modified to avoid collision by computing appropriate velocities for driving the robot motors. To the end, a part of implementation is presented to describes how the system works.

IA confiable del Instituto de Ingeniería Eléctrica y Electrónica | Reliable AI of the Institute of Electrical and Electronic Engineering

“AI will be such a program which in an arbitrary world will cope not worse than a human” (Dobrev 2004, 2); “Artificial intelli-gence is the enterprise of constructing a symbol sys-tem that can reliably pass the Turing test” (Ginsberg 2012, 9); See Figure 1.1 Russell and Norvig (1995 page 5). “Artificial intelli-gence is a field of com-puter science concerne dwith the computational understanding of what is commonly called intelli-gent behavior and with the creation of artifacts that exhibit such behav-ior. This definition may e examined more closely by considering the field from three points of view: computational psychology (the goal of which is to understand human intelli-gent behvaior by creating computer programs that behave in the same way that people do), computa-tional philosopy (the goal of which is to form a com-putational understanding of human-level intelligent behavior, without being resticted to the algorithms and data structures that the human mind actually does use), and machine intelligence (the goal of which is to expand the fronteir of what we know how to program” (Reilly 2004, 40-41).

Artificial intelligence

Artificial intelligence (AI) tries to make computer systems (of various kinds) do what minds can do: interpreting a photograph as depicting a face; offering medical diagnoses; using and translating language; learning to do better next time. AI has two main aims. One is technological: to build useful tools, which can help humans in activities of various kinds, or perform the activities for them. The other is psychological: to help us understand human (and animal) minds, or even intelligence in general. Computational psychology uses AI concepts and AI methods in formulating and testing its theories. Mental structures and processes are described in computational terms. Usually, the theories are clarified, and their predictions tested, by running them on a computer program. Whether people perform the equivalent task in the same way is another question, which psychological experiments may help to answer. AI has shown that the human mind is more complex than psychologists had previously assumed, and that introspectively ‘simple’ achievements – many shared with animals – are even more difficult to mimic artificially than are ’higher’ functions such as logic and mathematics. There are deep theoretical disputes within AI about how best to model intelligence. Classical (symbolic) AI programs consist of formal rules for manipulating formal symbols; these are carried out sequentially, one after the other. Connectionist systems, also called neural networks, perform many simple processes in parallel (simultaneously); most work in a way described not by lists of rules, but by differential equations. Hybrid systems combine aspects of classical and connectionist AI. More recent approaches seek to construct adaptive autonomous agents, whose behaviour is self-directed rather than imposed from outside and which adjust to environmental conditions. Situated robotics builds robots that react directly to environmental cues, instead of following complex internal plans as classical robots do. The programs, neural networks and robots of evolutionary AI are produced not by detailed human design, but by automatic evolution (variation and selection). Artificial life studies the emergence of order and adaptive behaviour in general and is closely related to AI. Philosophical problems central to AI include the following. Can classical or connectionist AI explain conceptualization and thinking? Can meaning be explained by AI? What sorts of mental representations are there (if any)? Can computers, or non-linguistic animals, have beliefs and desires? Could AI explain consciousness? Might intelligence be better explained by less intellectualistic approaches, based on the model of skills and know-how rather than explicit representation?

Animating Cognitive Models and Architectures: A Rule-Based Approach

Computational psychology provides computational models exploring different aspects of cognition. A cognitive architecture includes the basic aspects of any cognitive agent. It consists of different correlated modules. In general, cognitive architectures provide the needed layouts for building intelligent agents. The paper presents the a rule-based approach to visually animate the simulations of models done through cognitive architectures. As a proof of concept, simulations through Adaptive Control of Thought-Rational (ACT-R) were animated. ACT-R is a well-known cognitive architecture. It was deployed to create models in different fields including, among others, learning, problem solving and languages.

A Propósito da Naturalização da Dor na Obra de Filipe Montalto

The authors make a reading of the remarkable books on pain of the Arquipatologia of Filipe Montalto (1614). They interpret this work as the first naturalization and simultaneously the consequent defense of an emotional theory of pain and illness. They then briefly discuss aspects of the reductionist attitude about pain, and its implications on matters of mental content, reference and “computational psychology”.