The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

A corpus-based analysis of route instructions in human-robot interaction

This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was operated by naïve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well as the environment

Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind

In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive articial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, p. 17]. By using the Newell's own words: "To treat a system at the knowledge level is to treat it as having some knowledge, some goals, and believing it will do whatever is within its power to attain its goals, in so far as its knowledge indicates" [22, p. 13]. In the last decades, the importance of the knowledge level has been historically and system- atically downsized by the research area in cognitive architectures (CAs), whose interests have been mainly focused on the analysis and the development of mechanisms and the processes governing human and (articial) cognition. The knowledge level in CAs, however, represents a crucial level of analysis for the development of such articial general systems and therefore deserves greater research attention [17]. In the following, we will discuss areas of broad agree- ment and outline the main problematic aspects that should be faced within a Common Model of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly impact both lower (e.g. representational) and higher (e.g. social) levels

Cognition, institutions, near decomposability : rethinking Herbert Simon's contribution

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Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Most of the proposed approaches explore pattern recognition techniques which are generally based on hand-crafted features or learned features. In this paper, a novel Hierarchal Multichannel Deep Residual Network (HMResNet) model is proposed for robotic systems to recognize daily human activities in the ambient environments. The introduced model is comprised of multilevel fusion layers. The proposed Multichannel 1D Deep Residual Network model is, at the features level, combined with a Bottleneck MLP neural network to automatically extract robust features regardless of the hardware configuration and, at the decision level, is fully connected with an MLP neural network to recognize daily human activities. Empirical experiments on real-world datasets and an online demonstration are used for validating the proposed model. Results demonstrated that the proposed model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

DECISION ENVIRONMENTS TO ENCOURAGE MORE SUSTAINABLE INFRASTRUCTURE OUTCOMES

Physical infrastructure (i.e. roads, pipelines, airports, dams, landfills, and water treatment systems) contributes directly to sustainability outcomes such as energy and water use and climate changing emissions. The infrastructure built today will likely impact future generations for many years. Planning, design and development decisions about infrastructure are critical to the future performance of these systems. Such decisions about infrastructure are complex with multiple variables, alternative options, and design stages. To manage decisions that exceed cognitive capacity to consider all options, decision makers often create mental shortcuts (heuristics), and accompanied errors (biases). The potential cognitive biases when dealing with complex decisions about infrastructure are examined and an approach to reframe the decision process during infrastructure planning is explored. A more critical analysis is then provided for decision aids, like energy codes and rating metrics (e.g. LEED and Envision), which are intended to reduce complexity and improve decision making using set goals and scaled points for achieving predefined objectives in sustainability. However, unintentionally, these tools may create additional biases that limit the higher achievements in sustainability that are possible. For instance, framing a decision as a loss, rather than a gain, in value can reduce the decision makers\u27 acceptance of risk and, in turn, influence the outcome. The Envision rating system for sustainable infrastructure is presented to measure the influence of framing effects on engineering decision environments. Envision\u27s current framework, starts users with zero points and points are achieved when design considerations move beyond conventional construction standards. In a modified version of Envision, a higher benchmark is set. Users are endowed points and can lose points for not maintaining high consideration for sustainability. Students (n=41) and professional engineers (n=65) were randomly assigned the replica Envision software or the modified version endowing points. Participants were asked to make design considerations for a redevelopment project using Envision. The results indicate, the endowed version significantly improved students\u27 and professional engineers\u27 consideration for sustainability design achievement. The student participants that were endowed points (n=16) scored 63 percent of possible points compared to the standard group\u27s (n=25) 44 percent (p=0.002). The professional engineers that were endowed points (n=32) achieved 66 percent of possible points compared to the standard group\u27s (n=33) 51 percent (p=0.002). Both students and professional engineers that were endowed points acted loss averse trying to maintain the initial points in sustainability given. These findings suggest engineers\u27 process design decisions by comparing alternative options. And options framed as a loss or gain in value affects the decision outcome. This research underscores the advances possible at the intersection of behavioral science and engineering for sustainability. Slight changes in framing decision aids can lead to greater achievement in sustainability, and at a relatively low cost to implement. Future research should continue to explore how engineers make decisions and what behavioral and decision theories can merge with engineering to encourage more sustainable infrastructure outcomes

A conceptual framework for interactive virtual storytelling

This paper presents a framework of an interactive storytelling system. It can integrate five components: management centre, evaluation centre, intelligent virtual agent, intelligent virtual environment, and users, making possible interactive solutions where the communication among these components is conducted in a rational and intelligent way. Environment plays an important role in providing heuristic information for agents through communicating with the management centre. The main idea is based on the principle of heuristic guiding of the behaviour of intelligent agents for guaranteeing the unexpectedness and consistent themes