Model-driven visualization (MDV) is a novel framework that supports more effective, intelligent user interfaces to improve decision making in complex environments by coupling cognitive and perceptual theories of information processing with advanced artificial intelligence methods. It embeds empirical and theory driven approaches for identifying and prioritizing data based on the information requirements and needs of the human decision maker within intelligent agents. The agents automatically deliver and present information based on its likely value using visualizations that best convey that information to the user(s) of the system. Agents also reason about the context and constraints of the user, environment, and display to enable a higher degree of personalization within an interactive user interface (e.g., by drawing a user’s attention to interesting aspects of the data such as trends, anomalies, and patterns). We apply cognitive systems engineering processes to help identify the information available to individuals and/or teams, where it resides, where it is needed, and ultimately how to create the mappings required in connecting critical information to those who need it with innovative visualizations that most effectively support the end user. This paper describes the application of MDV to intelligently deliver timely, mission-critical information by adapting a Common Tactical Picture (CTP) display used for maritime situation awareness, threat assessment, and decision support.

Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before).

In many multiagent scenarios, groups of participants (known as coalitions) may attempt to cooperate, seeking to increase the benefits realized by the members. Depending on the scenario, such cooperation may be benign, or may be unwelcome or even forbidden (often called collusion). Coalitions can present a problem for many multiagent systems, potentially undermining the intended operation of systems. In this paper, we present a technique for detecting the presence of coalitions (malicious or otherwise), and identifying their members. Our technique employs clustering in benefit space, a high-dimensional feature space reflecting the benefit flowing between agents, in order to identify groups of agents who are similar in terms of the agents they are favoring. A statistical approach is then used to characterize candidate clusters, identifying as coalitions those groups that favor their own members to a much greater degree than the general population. We believe that our approach is applicable to a wide range of domains. Here, we demonstrate its effectiveness within a simulated marketplace making use of a trust and reputation system to cope with dishonest sellers. Many trust and reputation proposals readily acknowledge their ineffectiveness in the face of collusion, providing one example of the importance of the problem. While certain aspects of coalitions have received significant attention (e.g., formation, stability, etc.), relatively little research has focused on the problem of coalition identification. We believe our research represents an important step towards addressing the challenges posed by coalitions.

An ad hoc team setting is one in which teammates must work together to obtain a common goal, but without any prior agreement regarding how to work together. In this paper we present a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role that accomplishes a specific task or exhibits a particular behavior. In such cases, the role an ad hoc agent should select depends both on its own capabilities and on the roles currently selected by the other team members. We formally define methods for evaluating the influence of the ad hoc agent's role selection on the team's utility, leading to an efficient calculation of the role that yields maximal team utility. In simple teamwork settings, we demonstrate that the optimal role assignment can be easily determined. However, in complex environments, where it is not trivial to determine the optimal role assignment, we examine empirically the best suited method for role assignment. Finally, we show that the methods we describe have a predictive nature. As such, once an appropriate assignment method is determined for a domain, it can be used successfully in new tasks that the team has not encountered before and for which only limited prior experience is available.

We analyze the patterns of autistic reasoning while performing planning tasks. The formalism of non-monotonic logic of defaults is used to simulate the autistic decision-making while adjusting an action to a context. Our current main finding is that while people with autism may be able to process single default rules, they have a characteristic difficulty in cases where multiple default rules conflict. Even though default reasoning was intended to simulate the reasoning of typical human subjects, it turns out that following the operational semantics of default reasoning in a literal way leads to the peculiarities of autistic behavior observed in the literature.

Conceptually founded on the "patternist" systems theory of intelligence outlined in (Goertzel 2006), OCP combines Defining Forgetting In ordinary human discourse, the multiple AI paradigms such as uncertain logic, computational word "forget" has multiple shades of meaning. It can refer linguistics, evolutionary program learning and connectionist to the irreversible elimination of a certain knowledge item attention allocation in a unified architecture. Cognitive from memory; or it can mean something milder, as in cases processes embodying these different paradigms interoperate where someone "forgets" something, but then remembers it together on a common neural-symbolic knowledge shortly after. In the latter case, "forgetting" means that the store called the Atomspace. The interaction of these processes knowledge item has been stored in some portion of memory is designed to encourage the self-organizing emergence from which access is slow and uncertain.

When a new-born animal (agent) opens its eyes, what it sees is a patchwork of light and dark patterns, the natural scene.What is perceived by the agent at this moment is based on the patternof neural spikes in its brain. Life-long learning begins with such a flood of spikes in the brain. All knowledge and skills learned by the agent are mediated by such spikes, thus it is critical to understand what information these spikes convey and how they can be used to generate meaningful behavior. Here, we consider how agents can autonomously understand the meaning of these spikes without direct reference to the stimulus. We find that this problem, the problem of grounding, is unsolvable if the agent is passively perceiving, and that it can be solved only through self-initiated action. Furthermore, we show that a simple criterion, combined with standard reinforcement learning, can help solve this problem. We will present simulation results and discuss the implications of these results on life-long learning.

Autonomous robots demand complex behavior to deal with unstructured environments. To meet these expectations, a robot needs to address a suite of problems associated with long term knowledge acquisition, representation, and execution in the presence of partial information. In this paper, we address these issues by the acquisition of broad, domain general skills using an intrinsically motivated reward function. We show how these skills can be represented compactly and used hierarchically to obtain complex manipulation skills. We further present a Bayesian model using the learned skills to model objects in the world, in terms of the actions they afford. We argue that our knowledge representation allows a robot to both predict the dynamics of objects in the world as well as recognize them.

In 2009, an average American spent 3 hours per day watching TV. Recent advances in TV entertainment technologies, such as on-demand content, browsing the Internet, and 3D displays, have changed the traditional role of the TV and turned it into the center of home entertainment. Most of these technologies are personal and would benefit from seamless identification of who sits in front of the TV. In this work, we propose a practical and highly accurate solution to this problem. This solution uses a camera, which is mounted on a TV, to recognize faces of people in front of the TV. To make the approach practical, we employ online learning on graphs and show that we can learn highly accurate face models in difficult circumstances from as little as one labeled example. To evaluate our solutions, we collected a 10-hour long dataset of 8 people who watch TV. Our precision and recall are in the upper nineties, and show the promise of utilizing our approach in an embedded setting.

Recently, information gain has been proposed as a candidate intrinsic motivation for lifelong learning agents that may not always have a specific task.  In the InfoMax control framework, reinforcement learning is used to find a control policy for a POMDP in which movement and sensing actions are selected to reduce Shannon entropy as quickly as possible. In this study, we implement InfoMax control on a robot which can move between objects and perform sound-producing manipulations on them.  We formulate a novel latent variable mixture model for acoustic similarities and learn InfoMax polices that allow the robot to rapidly reduce uncertainty about the categories of the objects in a room. We find that InfoMax with our improved acoustic model leads to policies which lead to high classification accuracy.  Interestingly, we also find that with an insufficient model, the InfoMax policy eventually learns to "bury its head in the sand" to avoid getting additional evidence that might increase uncertainty.  We discuss the implications of this finding for InfoMax as a principle of intrinsic motivation in lifelong learning agents.