The agent is "always on," i.e. it is continuously available and aware (using a camera and infrared motion sensor) when the user is in its presence and can initiate interaction with the user, rather than requiring the user login to begin interaction. We expect that the agent will help reduce the user's isolation not just by always being around but also by specific activities that connect the user with friends, family and the local community. Our goal is for the agent to be a natural, humanlike presence that "resides" in the user's apartment. Beginning in the late summer of 2014, we will be placing our agents with users for a monthlong evaluation study. Figure 1: Virtual agent interface -- "Karen" Three issues of our project directly concern the topics of this workshop are: (1) the embodiment of the agent, (2) the engagement behaviors that are associated with being "always measures we will be using are questionnaires that assess the on," and (3) AI tools for support intelligent behavior.

We give an overview of how the CADENCE architecture addresses the problem of turn-taking in embodied interaction.

Important aspects of human cognition, like creativity and play, involve dealing with multiple divergent views of objects, goals, and plans. We argue in this paper that the current model of optimization that drives much of modern machine learning research is far too restrictive a paradigm to mathematically model the richness of human cognition. Instead, we propose a much more flexible and powerful framework of equilibration, which not only generalizes optimization, but also captures a rich variety of other problems, from game theory, complementarity problems, network equilibrium problems in economics, and equation solving. Our thesis is that creative activity involves dealing not with a single objective function, which optimization requires, but rather balancing multiple divergent and possibly contradictory goals. Such modes of cognition are better modeled using the framework of variational inequalities (VIs). We provide a brief review of this paradigm for readers unfamiliar with the underlying mathematics, and sketch out how VIs can account for creativity and play in human and animal cognition.

Shared mental models have been shown to improve human team performance. We thus conjecture that shared mental models (SMMs) integrated into cognitive robotic architectures might also improve the performance of mixed human-robot teams. To date, very little research has focused on developing appropriate computational constructs that can support domain independence and generalizability, while also being scalable. In this paper, we outline our proposed development of SMMs for cognitive robots.

In this work, we focus on advancing the state of the art in intelligent agents that can learn complex procedural tasks from humans. Our main innovation is to view the interaction between the human and the robot as a mixed- initiative collaboration. Our contribution is to integrate hierarchical task networks and collaborative discourse theory into the learning from demonstration paradigm to enable robots to learn complex tasks in collaboration with the human teacher.

Efforts to study computational aspects of the conscious mind have made substantial progress, but have yet to provide a compelling route to creating a phenomenally conscious machine. Here I suggest that an important reason for this is the computational explanatory gap: our inability to explain the implementation of high level cognitive algorithms that are of interest in AI in terms of neurocomputational processing. Bridging this gap could contribute to further progress in machine consciousness, to producing artificial general intelligence, and to understanding the fundamental nature of consciousness.

AI has a long history of making fine tools, and an equally long history of trying to simulate human intelligence, without, I contend, really understanding what intelligence consists in: the ability to deal with the world, which presupposes having a stake in one's own being. The tools are very nifty, but I don't see how it is even possible to simulate having a stake in one's own being.

Can brain-mapping data be used to reverse engineer a brain Noam Chomsky discusses the evolution of the field of system in silico? This is actually the question of whether artificial intelligence from 1956, when John McCarthy consciousness is fully contained within the physical defined the science, until today (Ramsay, 2012). The goal structure that is the brain. Do the brain and its supporting of AI was to study intelligence by implementing its systems fully account for consciousness or are there other essential features using man-made technology. This goal components that transcend the body that are also at play? If has resulted in several practical applications people use metaphysical components play a role, then the answer is every day. The field has produced significant advances in negative, since mapping just the anatomical aspects of the search engines, data mining, speech recognition, image consciousness system would leave a critical component processing, and expert systems, to name a few.

Connecting the dots between discoveries in neuroscience(neuroplasticity), psychoneuroimmunology(the brain-immune loop) and user experience (gadget rub-off) indicate the nature of our time spent with gadgets is a vector in human health - mentally, socially and physically. The positive design of our interactions with devices therefore can have a positive impact on economy, civilization and society. Likewise, the absence of design that encourages positive interaction may encourage undesirable behaviors. Much like the architecture of physical spaces and buildings, the consequences of the architecture of the 21stcentury conversation between man and machine may last generations. AI and the Internet of Things are primary vectors for positive and negative impacts of technology.  We describe a growing body of co-discoveries occurring across a variety of disciplines that support the argument for human sciences in technology design.

Research in neurally-based machine (i.e. computational) systems is expanding. “Reverse-engineered” models of brain-like structures are viable candidates for developing increasing complexification (via generatively encoded “intelligence”) that could instantiate some form of consciousness – albeit not identical to human consciousness. This essay posits how such trajectories could lead to the iterative development of “machine sentience” and addresses issues of what “machine consciousness” might mean for: 1) the ways that humans regard such machine entities as “beings” and/or “persons”, and 2) philosophical, ethical and socio-legal positions which might need to be adapted to guide and govern human treatment of, and interactions with such entities. Herein, I argue that neuroethics contributes crucial insights and viable tools to any meaningful approach to this topic (in synergy with extant discourse in “robo-ethics”). As the fields of neuro- and cognitive science, and computational engineering become increasingly convergent, so too must the philosophical and ethical approaches that can – and should – be employed to direct what convergent science may create. The speed and breadth of such technological development are such that neuroethical address and engagement of these issues and questions must be equivalently paced and iterative, so as to retain preparatory value.