Adaptable Robots

In this essay we consider some of the characteristics of adaptive biological systems and how these might work as models in designing a robot intended for the exploration of complex environments. Trying to design a robot that has such properties forces one to think hard about the nature of those properties. Here we have one intersection between philosophy and computing. We consider the nature of adaptability and some properties of complex biological systems that are relevant to designing adaptive robots, including direct perception, animacy, affordances, redundant degress of freedom, collective decisions, and emergent properties. We explain how these concepts were used in the development of a robotic arm.     

Précis of The brain and emotion

The topics treated in The brain and emotion include the definition, nature, and functions of emotion (Ch. 3); the neural bases of emotion (Ch. 4); reward, punishment, and emotion in brain design (Ch. 10); a theory of consciousness and its application to understanding emotion and pleasure (Ch. 9); and neural networks and emotion-related learning (Appendix). The approach is that emotions can be considered as states elicited by reinforcers (rewards and punishers). This approach helps with understanding the functions of emotion, with classifying different emotions, and in understanding what information-processing systems in the brain are involved in emotion, and how they are involved. The hypothesis is developed that brains are designed around reward- and punishment-evaluation systems, because this is the way that genes can build a complex system that will produce appropriate but flexible behavior to increase fitness (Ch. 10). By specifying goals rather than particular behavioral patterns of responses, genes leave much more open the possible behavioral strategies that might be required to increase fitness. The importance of reward and punishment systems in brain design also provides a basis for understanding the brain mechanisms of motivation, as described in Chapters 2 for appetite and feeding, 5 for brain-stimulation reward, 6 for addiction, 7 for thirst, and 8 for sexual behavior.     

The role of cognitive architectures in general artificial intelligence

The term “Cognitive Architectures” indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research.     

Building human-like communicative intelligence: A grounded perspective

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, “grounded”, perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building “grounded” linguistic intelligence. These components include embodying machines in a perception–action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.     

Executive Function: The Search for an Integrated Account

ABSTRACT— In general, executive function can be thought of as the set of abilities required to effortfully guide behavior toward a goal, especially in nonroutine situations. Psychologists are interested in expanding the understanding of executive function because it is thought to be a key process in intelligent behavior, it is compromised in a variety of psychiatric and neurological disorders, it varies across the life span, and it affects performance in complicated environments, such as the cockpits of advanced aircraft. This article provides a brief introduction to the concept of executive function and discusses how it is assessed and the conditions under which it is compromised. A short overview of the diverse theoretical viewpoints regarding its psychological and biological underpinnings is also provided. The article concludes with a consideration of how a multilevel approach may provide a more integrated account of executive function than has been previously available.     

The evolutionary basis of risky adolescent behavior: implications for science, policy, and practice

This article proposes an evolutionary model of risky behavior in adolescence and contrasts it with the prevailing developmental psychopathology model. The evolutionary model contends that understanding the evolutionary functions of adolescence is critical to explaining why adolescents engage in risky behavior and that successful intervention depends on working with, instead of against, adolescent goals and motivations. The current article articulates 5 key evolutionary insights into risky adolescent behavior: (a) The adolescent transition is an inflection point in development of social status and reproductive trajectories; (b) interventions need to address the adaptive functions of risky and aggressive behaviors like bullying; (c) risky adolescent behavior adaptively calibrates over development to match both harsh and unpredictable environmental conditions; (d) understanding evolved sex differences is critical for understanding the psychology of risky behavior; and (e) mismatches between current and past environments can dysregulate adolescent behavior, as demonstrated by age-segregated social groupings. The evolutionary model has broad implications for designing interventions for high-risk youth and suggests new directions for research that have not been forthcoming from other perspectives.     

A bio-inspired model of behavior considering decision-making and planning,spatial attention and basic motor commands processes

Cognitive architectures (CA) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent’s motor behavior.     

Artificial Intelligence and the New Alienation of Human Beings

Artificial intelligence (AI) is a revolutionary and overwhelming technology that is yet to immature. While profoundly changing and shaping people and society, AI also splits into its own opposites and develops into a new external alien force. As the basic technical support of the entire society, intelligent technology entails the overt or covert domination of human beings, who are becoming the “vassals” and “slaves” of this high-speed intelligent social system. Various intelligent systems are constantly replacing human work, so that the “digital poor” gradually lose the opportunities and values offered by labor and hence are excluded by the global economic and social system, rendering their existence empty and absurd. The rapid development of intelligent robots has blurred the boundary between humans and machines and had a strong impact on the nature of man and his position as a conscious agent, making “What is man?” and the human-machine relationship prominent issues for our times, challenging the commonplaces of philosophy. We must face up to the existing or imminent risk of alienation, expand our theoretical horizons, innovate theories of alienation in the era of intelligence, take constructive action in terms of the construction of an ideal society and the evolution of man himself, build an ecological system for the joint evolution and growth of human beings and intelligent machines, and achieve liberty of man and the all-round and free development.     

A real‐world rational agent: unifying old and new AI

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.     

Multimodal interactions in typically and atypically developing children: natural versus artificial environments

This review addresses the central role played by multimodal interactions in neurocognitive development. We first analyzed our studies of multimodal verbal and nonverbal cognition and emotional interactions within neuronal, that is, natural environments in typically developing children. We then tried to relate them to the topic of creating artificial environments using mobile toy robots to neurorehabilitate severely autistic children. By doing so, both neural/natural and artificial environments are considered as the basis of neuronal organization and reorganization. The common thread underlying the thinking behind this approach revolves around the brain’s intrinsic properties: neuroplasticity and the fact that the brain is neurodynamic. In our approach, neural organization and reorganization using natural or artificial environments aspires to bring computational perspectives into cognitive developmental neuroscience.     

Neural predictive mechanisms and their role in cognitive incrementalism

Many neuroscientists view prediction as one of the core brain functions, especially on account of its support of fast movements in complex environments. This leads to the natural question whether predictive knowledge forms the cornerstone of our common-sense understanding of the world. However, there is little consensus as to the exact nature of predictive information and processes, or of the neural mechanisms that realize them. This paper compares procedural versus declarative notions of prediction, examines how the brain appears to carry out predictive functions, and discusses to what degree, and at what level, these neural mechanisms support cognitive incrementalism: the notion that high-level cognition stems from sensorimotor behavior.     

Extending adaptive world modeling by identifying and handling insufficient knowledge models

Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.     

Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies

This article reviews map-learning and path-planning strategies within the context of map-based navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, which must be tightly tailored to the specific robot that is used, notably to the capacities and limitations of its sensory-motor equipment, and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot’s way to its goal.     

Multilayer cognitive architecture for UAV control

Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation, etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modeling, control signal generation, location and mapping, path planning, etc. with the methods of behavior modeling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture—STRL (strategic, tactical, reactive, layered)—of the control system that automates the behavior generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV). We use a special type of knowledge representation—sign world model—that is based on the psychological activity theory to describe individual behavior planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAV’s coalition automatically, as well as raise the autonomy level of each individual member of that coalition.     

Kognitive Lernermodellierung

Different types of learner models and their usefulness for tutoring have been discussed widely since the beginning of intelligent tutoring systems. In this paper we compare pragmatic and cognitive approaches of learner modeling. Pragmatic approaches consider relevant learner features for adaptive methods in learning environments and adapt different aspects of instruction to a restricted model representing these features. Cognitive approaches aim for a psychologically adequate modeling of human problem solving. We introduce the case-based learner model ELM as an example of a cognitive approach to learner modeling. The learning environments ELM-PE and ELM-ART use ELM for adaptional methods on conceptual, plan, and episodic levels and provide individual help and learning support. Especially in the case of integrated learning environments like ELM-ART which support a variety of learning activities, a combination of pragmatic and cognitive learner models is proposed to be a necessary and useful solution.     

When AI meets PC: exploring the implications of workplace social robots and a human-robot psychological contract

ABSTRACT

The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely future psychological contract partners for human employees, given these entities transform notions of workplace technology from being a tool to being an active partner. We first overview the increasing role of robots in the workplace, particularly through the advent of sociable AI, and synthesize the literature on human–robot interaction. We then develop an account of a human-social robot psychological contract and zoom in on the implications of this exchange for the enactment of reciprocity. Given the future-focused nature of our work we utilize a thought experiment, a commonly used form of conceptual and mental model reasoning, to expand on our theorizing. We then outline potential implications of human-social robot psychological contracts and offer a range of pathways for future research.     

Role of Psychologists in Academic Medicine

With an interdisciplinary approach to understanding human psychosocial development and behavior, psychologists in academic medical settings have much to offer in the areas of administration, research, teaching, and service, including in the realm of organizational systems and behavior within medical schools. In these ways, psychologists can play a large role in the fulfillment of the mission of academic medicine and enhance psychology’s stature in the medical school organization. Two case studies are presented that exemplify the varied roles and responsibilities of psychologists in a medical school. To be successful within medical schools, the following generalizable principles are offered: (1) “add value” to the medical school by demonstrating the diverse and relatively unique abilities of psychologists; (2) be flexible and adaptive to the different requirements of medical academic settings; (3) make a concerted effort to work well with medical students, residents, and physicians who may not be well-versed in certain aspects such as rigorous research methodology and statistics; and (4) find a “good match” between one’s strengths, the needs of the medical setting, and the attributes of the M.D. leader. With such an approach, remarkable accomplishments can be made.     

Mechanistic versus phenomenal embodiment: Can robot embodiment lead to strong AI?

Embodiment has become the raison d’etre for much of the new ‘cognitive robotics’. It fills a gap in the non-interactivist approach of traditional artificial intelligence (AI) in which ‘intelligence’ is viewed as the manipulation of symbols in a vacuum. However, a foundational question for the new AI is, can embodiment lead to a strong AI, i.e. a robot mind? To address this question, two extreme poles of embodiment are distinguished here, mechanistic and phenomenal. A detailed exploration of each type of embodiment is provided together with an appraisal of whether strong embodiment is possible for robotics, or whether robotics merely provides a tool for scientific exploration and modelling, i.e. weak embodiment? It is argued that strong embodiment, either mechanistic or phenomenal, is not possible for present day robots. However, weak embodiment may provide an enlightened approach to using robots for modelling cognition.     

Challenges for interactivist-constructivist robotics

The interactivist-constructivist (IC) approach offers an attractive framework for the development of intelligent robots. However, we still lack genuinely intelligent robots, capable of representing the world, in the IC sense. Here we argue that the reason for this situation is the lack of learning mechanisms that would allow the components of the robotic controller to learn constructively while they direct the robot's action in accordance to its value system. We also suggest that spike-timing-dependent plasticity (STDP) may be such a learning mechanism that operates in the brain.     

Developmental impact of frontal lobe injury in middle childhood.

Executive function of frontal lobe systems, like intelligent behavior, appears to demonstrate two major features: it is adaptive and goal-directed. Disruption of executive function following injury to the frontal lobes in childhood might be predicted to impact the trajectory of normal cognitive, behavioral, and social development. Case studies of two children injured at different ages showed the primary developmental impact to involve the behavioral and social realms. While cognitive development may be suspected to also be considerably influenced, assessment of changes in cognitive abilities by traditional psychometric means was found to be problematic. Psychometric changes were found to occur over a prolonged period of time, emphasizing the importance of documenting observations and chronicling postinjury events.