TEACHING FOSTER GRANDPARENTS TO TRAIN SEVERELY HANDICAPPED PERSONS

Five foster grandparents were taught training skills for use in their daily interactions with severely handicapped persons in an institution. Following baseline, specific teaching procedures consisting of teacher instructions, prompts, modelling, and praise were implemented. The grandparents' frequency of training three skill areas increased as the specific teaching was implemented in multiple-baseline format. The total amount of training continued as teacher instructions, prompts, and modelling were terminated and praise continued, although the grandparents spent their training time emphasizing only two of the three skill areas. Teacher presence was gradually reduced over an 11-week period, with no decrease in grandparents' frequency of training. Four of the foster grandchildren, all profoundly retarded and multiply handicapped, demonstrated progress throughout the study. Results were discussed in light of the available contributions of foster grandparents in institutional settings and maintenance of staff training.     

TRAINING PRESCHOOL CHILDREN TO RECRUIT NATURAL COMMUNITIES OF REINFORCEMENT1

Four normal and four deviant children aged four-to-six years were taught to judge the quality of their academic work in a preschool classroom, and to prompt or cue their teachers to comment about the quality of that work. When these skills did not generalize spontaneously to other teachers in concurrent natural situations, generalized responding was taught by the experimenter, in multiple-baseline design across subjects. This generalization programming enabled the children to contact a sometimes dormant, but readily available natural community of teacher praise and reinforcement, i.e., to recruit an increase in cued praise and schedules of praise for their good work. These behaviors may be important to young children who find themselves bereft of attention in classrooms.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Toward ethical cognitive architectures for the development of artificial moral agents

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.     

What Does God Know? Supernatural Agents’ Access to Socially Strategic and Non‐Strategic Information

Current evolutionary and cognitive theories of religion posit that supernatural agent concepts emerge from cognitive systems such as theory of mind and social cognition. Some argue that these concepts evolved to maintain social order by minimizing antisocial behavior. If these theories are correct, then people should process information about supernatural agents' socially strategic knowledge more quickly than non-strategic knowledge. Furthermore, agents' knowledge of immoral and uncooperative social behaviors should be especially accessible to people. To examine these hypotheses, we measured response-times to questions about the knowledge attributed to four different agents--God, Santa Claus, a fictional surveillance government, and omniscient but non-interfering aliens--that vary in their omniscience, moral concern, ability to punish, and how supernatural they are. As anticipated, participants respond more quickly to questions about agents' socially strategic knowledge than non-strategic knowledge, but only when agents are able to punish.     

Evolution of the Icarus Cognitive Architecture

Cognitive architectures serve as both unified theories of the mind and as computational infrastructures for constructing intelligent agents. In this article, we review the evolution of one such framework, Icarus, over the three decades of its development. We discuss the representational and processing assumptions made by different versions of the architecture, their relation to alternative theories, and some promising directions for future research.     

Subjective Situations and Logical Omniscience

The beliefs of the agents in a multi-agent system have been formally modelled in the last decades using doxastic logics. The possible worlds model and its associated Kripke semantics provide an intuitive semantics for these logics, but they commit us to model agents that are logically omniscient. We propose a way of avoiding this problem, using a new kind of entities called subjective situations. We define a new doxastic logic based on these entities and we show how the belief operators have some desirable properties, while avoiding logical omniscience. A comparison with two well-known proposals (Levesque's logic of explicit and implicit beliefs and Thijsse's hybrid sieve systems) is also provided.     

Neuropsychological Performance in Gulf War Era Veterans: Traumatic Stress Symptomatology and Exposure to Chemical–Biological Warfare Agents

Because complaints of diminished concentration and memory are among the most common health symptoms reported by Gulf War (GW) veterans with unexplained illnesses, this study investigated neuropsychological functions among GW veterans and controls. Relationships between neuropsychological performance, severity of posttraumatic stress disorder (PTSD) symptomatology, and exposure to chemical–biological warfare agents (CBW) were assessed. Participants were 225 veterans recruited from three cohorts: GW-deployed veterans from Ft. Devens, MA ( n = 141) and New Orleans, LA (n = 37), and Germany-deployed veterans from a Maine National Guard unit (n = 47). A comprehensive evaluation was completed. Severity of subclinical PTSD symptomatology was significantly related to scores on specific neuropsychological tests. PTSD symptom severity in GW-deployed veterans was found to be greater and associated with a broader range of neuropsychological deficits than in Germany-deployed veterans. PTSD severity was associated with lower performance on a range of neuropsychological tasks, whereas CBW exposure contributed to performance deficits on specific cognitive tasks.     

Representing decision-makers using styles of behavior: An approach designed for group decision support systems

Supporting decision-making processes when the elements of a group are geographically dispersed and on a tight schedule is a complex task. Aiming to support decision-makers anytime and anywhere, Web-based group decision support systems have been studied. However, the limitations in the decision-makers’ interactions associated to this scenario bring new challenges. In this work, we propose a set of behavioral styles from which decision-makers’ intentions can be modelled into agents. The goal is that, besides having agents represent typical preferences of the decision-makers (towards alternatives and criteria), they can also represent their intentions. To do so, we conducted a survey with 64 participants in order to find homogeneous operating values so as to numerically define the proposed behavioral styles in four dimensions. In addition, we also propose a communication model that simulates the dialogues made by decision-makers in face-to-face meetings. We developed a prototype to simulate decision scenarios and found that agents are capable of acting according to the decision-makers’ intentions and fundamentally benefit from different possible behavioral styles, just as a face-to-face meeting benefits from the heterogeneity of its participants.