Emergent intentionality in perception-action subsumption hierarchies

A cognitively-autonomous artificial agent may be defined as one able to modify both its external world-model and the framework by which it represents the world, requiring two simultaneous optimization objectives. This presents deep epistemological issues centered on the question of how a framework for representation (as opposed to the entities it represents) may be objectively validated. In this summary paper, formalizing previous work in this field, it is argued that subsumptive perception-action learning has the capacity to resolve these issues by {\em a)} building the perceptual hierarchy from the bottom up so as to ground all proposed representations and {\em b)} maintaining a bijective coupling between proposed percepts and projected action possibilities to ensure empirical falsifiability of these grounded representations. In doing so, we will show that such subsumptive perception-action learners intrinsically incorporate a model for how intentionality emerges from randomized exploratory activity in the form of 'motor babbling'. Moreover, such a model of intentionality also naturally translates into a model for human-computer interfacing that makes minimal assumptions as to cognitive states

Emergent intentionality in perception-action subsumption hierarchies

A cognitively-autonomous artificial agent may be defined as one able to modify both its external world-model and the framework by which it represents the world, requiring two simultaneous optimization objectives. This presents deep epistemological issues centered on the question of how a framework for representation (as opposed to the entities it represents) may be objectively validated. In this summary paper, formalizing previous work in this field, it is argued that subsumptive perception-action learning has the capacity to resolve these issues by {\em a)} building the perceptual hierarchy from the bottom up so as to ground all proposed representations and {\em b)} maintaining a bijective coupling between proposed percepts and projected action possibilities to ensure empirical falsifiability of these grounded representations. In doing so, we will show that such subsumptive perception-action learners intrinsically incorporate a model for how intentionality emerges from randomized exploratory activity in the form of 'motor babbling'. Moreover, such a model of intentionality also naturally translates into a model for human-computer interfacing that makes minimal assumptions as to cognitive states

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

The Role of Trust in Explaining Food Choice: Combining Choice Experiment and Attribute Best−Worst Scaling

This paper presents empirical findings from a combination of two elicitation techniques—discrete choice experiment (DCE) and best–worst scaling (BWS)—to provide information about the role of consumers’ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participants’ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumers’ attitude and trust significantly explain class membership and therefore, consumers’ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy

Representational fluidity in embodied (artificial) cognition

Theories of embodied cognition agree that the body plays some role in human cognition, but disagree on the precise nature of this role. While it is (together with the environment) fundamentally engrained in the so-called 4E (or multi-E) cognition stance, there also exists interpretations wherein the body is merely an input/output interface for cognitive processes that are entirely computational. In the present paper, we show that even if one takes such a strong computationalist position, the role of the body must be more than an interface to the world. To achieve human cognition, the computational mechanisms of a cognitive agent must be capable not only of appropriate reasoning over a given set of symbolic representations; they must in addition be capable of updating the representational framework itself (leading to the titular representational fluidity). We demonstrate this by considering the necessary properties that an artificial agent with these abilities need to possess. The core of the argument is that these updates must be falsifiable in the Popperian sense while simultaneously directing representational shifts in a direction that benefits the agent. We show that this is achieved by the progressive, bottom-up symbolic abstraction of low-level sensorimotor connections followed by top-down instantiation of testable perception-action hypotheses. We then discuss the fundamental limits of this representational updating capacity, concluding that only fully embodied learners exhibiting such a priori perception-action linkages are able to sufficiently ground spontaneously-generated symbolic representations and exhibit the full range of human cognitive capabilities. The present paper therefore has consequences both for the theoretical understanding of human cognition, and for the design of autonomous artificial agents

Entrepreneurship by circumstances and abilities: the mediating role of job satisfaction and moderating role of self-efficacy

Prior studies have found that job dissatisfaction and self-efficacy are significant factors influencing individuals’ entrepreneurial propensity. Existing literature on entrepreneurship often regards job dissatisfaction as an entrepreneurial push factor and self-efficacy as an entrepreneurial pull factor. The argument is that individuals who are dissatisfied with their jobs are more likely to seek alternative mode of employment such as self-employment. In other words, poor job circumstances may push individuals to leave their paid employment to start their own businesses. On the other hand, personal abilities such as self-efficacy may pull individuals toward starting their own businesses in areas where they are confident and competent in. Despite the importance of job dissatisfaction and self-efficacy for new venture creation, few if any studies have examined the entrepreneurial phenomena from a holistic perspective. Utilizing concepts from the P-E fit and self-efficacy literatures, this paper argues that the path to entrepreneurship is a multi-faceted interactive process between individuals’ personal attributes and their work environment. We specifically examined how IT professional’s personal attributes such as innovation orientation and self-efficacy condition individuals for an entrepreneurial career in unsatisfactory work environments.

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program