We present a novel integration between a computational framework for modeling attention-driven perception and cognition (ARCADIA) with a cognitive robotic architecture (DIARC), demonstrating how this integration can be used to drive the gaze behavior of a robotic platform. Although some previous approaches to controlling gaze behavior in robots during human-robot interactions have relied either on models of human visual attention or human cognition, ARCADIA provides a novel framework with an attentional mechanism that bridges both lower-level visual and higher-level cognitive processes. We demonstrate how this approach can produce more natural and human-like robot gaze behavior. In particular, we focus on how our approach can control gaze during an interactive object learning task. We present results from a pilot crowdsourced evaluation that investigates whether the gaze behavior produced during this task increases confidence that the robot has correctly learned each object.

Human behavior is frequently guided by social and moral norms, and no human community can exist without norms. Robots that enter human societies must therefore behave in norm-conforming ways as well. However, currently there is no solid cognitive or computational model available of how human norms are represented, activated, and learned. We provide a conceptual and psychological analysis of key properties of human norms and identify the demands these properties put on any artificial agent that incorporates norms-demands on the format of norm representations, their structured organization, and their learning algorithms.

The protean word `autonomous' has gained broad currency as a descriptive adjective for AI research projects, robotic and otherwise. Depending upon context, `autonomous' at present connotes anything from a shallow, purely reactive system to a sophisticated cognitive architecture reflective of much of human cognition; hence the term fails to pick out any specific set of constitutive functionality. However, philosophers and ethicists have something relatively well-defined in mind when they talk about the idea of autonomy. For them, an autonomous agent is often by definition potentially morally responsible for its actions. Moreover, as a prerequisite to correct ascription of `autonomous,' a certain capacity to choose freely is assumed - even if this freedom is understood to be semi-constrained by societal conventions, moral norms, and the like.

We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 1) Most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and 2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems.

An attempt is made to provide a new psychological mechanism, the mental possible worlds mechanism (MPWM), for analysing complex reasoning tasks such as the logical reasoning tasks in the Graduate Record Examination (GRE). MPWM captures the interaction between syntactic and semantic processes in reasoning, and so it also technically supports the new mental metalogic theory which studies the bridging relations between two major competing theories in the field, mental logic and mental models, whose accounts are also discussed. An empirical study of MPWM is also given.