The successful development of neural prostheses requires an understanding of the neurobiological bases of cognitive processes, i.e., how the collective activity of populations of neurons results in a higher-level process not predictable based on knowledge of the individual neurons and/or synapses alone. We have been studying and applying novel methods for representing nonlinear transformations of multiple spike train inputs (multiple time series of pulse train inputs) produced by synaptic and field interactions among multiple subclasses of neurons arrayed in multiple layers of incompletely connected units.

The challenge of designing a human-level learner is central to creating a computational equivalent of the human mind. It demands the level of robustness and flexibility of learning that is still only available in biological systems. Therefore, it is essential that we better understand at a computational level how biological systems naturally develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures (BICA) have emerged as a powerful new approach toward gaining this kind of understanding. The impressive success of BICA-2008 was clear evidence of this trend. As the second event in the series, BICA-2009 continues our attack on the challenge, with the overall atmosphere of excitement and promise, brainstorming, and collaboration.

In this work we will present an integration of a query-answering argumentation approach with an abstract agent programming language. Agents will argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

Automated health behavior coaches (HBCs) potentially can provide a widely accessible, cost-effective means of promoting health behavior. Coaches are intelligent agents that “converse” with users, offering tailored feedback, advice, and empathy. Research subjects like coaches and comply with target behaviors, but interest and adherence wane over time. More research is needed on next-generation HBCs to improve coaching techniques, enhance user engagement, and extend adherence. However, the necessary technical tools and expertise reside in only a few research labs. In an effort to expand and accelerate research, we are developing an HBC Kit that will extend and specialize our more general Imp™ Kit. We propose 7 innovations for next-generation HBCs, demonstrate them in a lifestyle coach, and characterize authoring with the Imp Kit. We discuss planned extensions for the HBC Kit to enable a larger and more diverse community to create and evaluate a broader range of coaches.

Individuals with high-functioning autism spectrum disorders (HFASD) have very individualistic needs, abilities, and are surrounded by very different social contexts. Consequently, special education and therapeutic interventions often need to be adapted to a particular individual. We are interested in developing systems that can help adolescents with HFASD rehearse and learn social skills with reduced aide from parents, guardians, teachers, and therapists. We describe a social skill learning game that utilizes social scenarios. Because of the individualistic needs and abilities of our target users, we describe ongoing work on AI to assist caregivers with the authoring of tailored social scenarios.

By moving from a health communication perspective, this paper addresses the issue of how to enhance consumers’ health literacy through virtual health environments. More specifically, the paper is structured in two parts. Firstly, we present a conceptualization of health literacy which takes into consideration the complexity of its components. Secondly, we show how this concept was used to design the website ONESELF targeted to consumers affected by chronic low back pain. Findings from our paper are expected to highlight important dimensions of health literacy that virtual healthcare systems – designed to enhance health literacy – will have to operationalise. ONESELF works through a bottom-up approach where users can ask for all information to build or reinforce their level of health literacy. This approach presupposes the physical presence of the content manager who assures the delivery of the information requested through the website. Here the main question arises of how AI systems can assure the same level of tailored information by standing, however, from a genuinely human-computer perspective

Long-term health monitoring is becoming increasingly important with the rising prevalence of chronic disease in the U.S. While many researchers are investigating the use of remote biological monitoring and telemedicine technologies, the use of frequent self-report in long-term health monitoring remains a relatively unstudied area. We discuss some of the many cognitive, affective and contextual issues that must be addressed in maintaining a long-term stream of quality data from patients at home or in the field, and how many of these issues can be addressed through the use of conversational agents.

This paper describes some functionalities and features of the Maryland Virtual Patient (MVP) environment. MVP models the process of disease progression, diagnosis and treatment in virtual patients who are endowed with a “body,” a simulation of their physiological and pathological processes, and a “mind,” a set of capabilities of perception, reasoning and action that allow the virtual patient to exhibit independent behavior, participate in a natural language dialog, remember events, hold beliefs about other agents and about specific object and event instances, make decisions and learn.