The classic Lotka-Volterra equations present a mathematically robust and well-validated set of idealized equations for describing the predator-prey relationship found in many domains. Here we present results of formulating these equations using a Complex Adaptive Systems model, simulated using Agent-based Modeling techniques. This method allows for (a) closer study of the complex dynamics that are found in these systems, (b) greater understanding of the agent interactions, and (c) more realistic simulation outputs. In so doing, we have uncovered a novel relationship between the amount of resources found at the lowest tropic level of a hypothesized ecosystem and the highest tropic level predators. We explore these results in detail, and highlight their applicability to a real-world marine ecosystem.

This paper describes Maryland Virtual Patient (MVP), a simulation and tutoring environment developed to support training cognitive decision making in clinical medicine. MVP is implemented as a society of agents, with one role – that of the trainee – played by a human and other roles played by artificial intelligent agents. In order to make the trainee’s experience as similar as possible to the traditional medical training environment, MVP is implemented as a collection of knowledge-based models of simulated human-like perception, reasoning and action processes. MVP operation involves metacognition: for example, the MVP virtual patient is aware of the physiological state of its body, of its physiological and character traits as well as of lacunae in its knowledge about the world and about language. This self-awareness influences the virtual patient’s reasoning and actions. In this paper we illustrate the role of metacognitive self-awareness in the overall operation of MVP.

Our learning by teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is structured so that successfully instructing their teachable agent requires the students to learn and understand science topics for themselves. This process is supported by adaptive scaffolding and feedback from the system. This feedback is instantiated through the interactions with the teachable agent and a mentor agent, named Mr. Davis. This paper provides an overview of two studies that were conducted with 5th grade science students and a description of the analysis techniques that we have developed for interpreting students’ activities in this learning environment.

While the application areas addressed typically - Social Interaction operate at a macroscopic scale and could not be considered quantum in a quantum mechanical sense, they may - Finance, economics, and social structures (e.g., organizations, share many key properties with quantum systems. Each paper was thoroughly reviewed by at problems with AI in non-quantum domains more efficiently least three members of the international programme committee. Kanerva (Stanford University), and an invited talk on day - Logic, planning, agents and multi-agent systems 2 by Terry Bollinger (ONR/MITRE). Finally, welcome and we look forward to a stimulating symposium!

Forming ad-hoc coalitions between military forces and humanitarian organizations is crucial in mission-critical scenarios. Very often coalition parties need to operate according to planning constraints and regulations, or policies. Therefore, they find themselves not only in need to consider their own goals, but also to support coalition partners to the extent allowed by such regulations. In time-stressed conditions, this is a challenging and cognition-intensive task. In this paper, we present intelligent agents that support human planners and ease their cognitive burden by detecting and giving advice about the violation of policies and constraints. Through a series of experiments conducted with human subjects, we compare and contrast the agents' performance on a number of metrics in three conditions: agent support, transparent policy enforcement, and neither support nor enforcement.

Diffusion distance has been shown to be significantlymore effective than Euclidean distance in multi-scalerecognition of similar experiences in Recognition-Primed Decision making In this paper, we first examine the experience data set used inthe previous study. The visualization of the data set(using the first three dominant eigenvectors of the diffusion space) suggests the applicability of the diffusion approach. Second, we investigate two approaches to the computation of diffusion distance: Spectrum based and Probability-Matching based. Specifically, by ‘Spectrumbased’ approach we refer to the one derived in terms of the eigenvalues/eigenvectors of the normalized diffusion matrix. We use the term ‘Probability-Matching’ to refer to the use of various probability distances, where the original L2 diffusion distance is treated as a special case. Our preliminary result indicates that the performance of using L2 diffusion distance at least is tied with the use of Spectrum based distance. Furthermore, when spectrum based approach is applied, we have to use the embedding and extending techniques for labeling new experience data, while such recomputation is not necessary when the L2 diffusion distance is used. We do not need to recompute the diffusion matrix, hence the diffusion map each time when adding a new data. It is more natural and robust especially for labeling new single experience data. The numerical examples also show the improvement on the performance. We are currently working on several other Probability-Matching approaches (e.g. the Earth-Mover’s Distance).

As robots become incorporated into our environments, they must be equipped with the ability to communicate effectively with us. In particular, robots that perform longer tasks for a small set of people (e.g., a companion robot to escort visitors to meetings all day) need to be able to start and maintain interesting and relevant dialog with any and all humans involved.In this work, we present our ongoing work on our robot, CoBot, which is assigned an all-day task to escort a visitor around our building and perform tasks for her. We first describe CoBot's dialog manager which is responsible for the task-oriented dialog, including dialog to meet the visitor's needs, CoBot's notifications of interesting locations around the building, and the robot's own requests for help. We, then, focus two aspects of the dialog manager: 1) how CoBot can invoke more accurate answers to its requests for help from the visitor and 2) how to reduce repetitive dialog which can happen during all-day interactions. We provide an example dialog between CoBot and a visitor to illustrate the dialog manager's capabilities.

We summarize our past, present and future research related to human-robot dialogue, starting with its foundations in collaborative discourse theory, continuing to our current research on recognizing and generating engagement, and concluding with an outline of new work we are beginning on the modeling of long-term relationships between humans and robots.

Among the many aspects of human intelligence that currently elude the simulation by machines is that of story understanding. Although many theories of narrative have been proposed, several processes pertaining to narrative remain inadequately formalized and, hence, beyond full mechanization. This work proposes a general formal framework that attempts to make precise such processes and related notions, with first and foremost that of what constitutes a narrative. Emphasis is placed on identifying certain premises that narratives are expected to adhere to, and deriving the formal implications that these have in terms of the computability of the various relevant notions. Among others, it is established that checking whether a discourse is a narrative is decidable, and that narratives can be computably enumerated and, hence, unambiguously indexed.

In this paper, we explore ideas regarding a formal logical model which allows for the use of stories to persuade autonomous software agents to take a particular course of action. This model will show how typical stories – sequences of events that form a meaningful whole – can be used to set an example for an agent and how the agent might adapt his own values and choices according to the values and choices made by the characters in the story.