Irregular Warfare (IW), with its emphasis on social and cognitive phenomena such as population sentiment, is a major new focus of the Department of Defense (DoD). One of the most important classes of IW action is Information Operations (IO), the use of information to influence sentiment. With the DoD’s new focus on IW comes the new need to analyze and forecast the effects of IO actions on population sentiment. Analysts at the DoD traditionally use Modeling and Simulation to analyze and forecast the effects of conventional warfare’s actions on the outcome of wars, but IW and IO in particular are far more complex than conventional physics-based simulations. DoD analysts are in the early stages of looking for scientifically rigorous methods in the Modeling and Simulation of IO’s complex effects. This paper presents the state of IO modeling and simulation in the DoD, using examples from several computer models now being used, in these early stages of IW analysis. It discusses how the ideas of Complex Adaptive Systems (CAS) and threshold events in particular may be incorporated into IO modeling in order to increase its scientific rigor, fidelity, and validity.

The objective of our research is to facilitate social conversation between persons affected with Alzheimer’s Disease (AD) and their caregivers via a future intervention for caregivers. In the intervention, a computer system will enable caregivers to practice spoken conversation with high-fidelity Virtual Patients simulating the verbal and non-verbal behavior of persons with AD (VP-AD). It is hoped that the skills acquired by the caregiver will improve the quality of life of persons with AD and reduce caregiver stress. In this paper, we describe a pilot study intended to evaluate the efficacy of active participation in conversation with a lower fidelity VP-AD in comparison to passive observation of the same VP-AD in conversation. The study found, after 15 minutes or less of practice, a significant increase in use of recommended caregiver communication skills by participants in the active condition.

By this paper we would like to open a discussion on the need ofBy this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter.

Thought is an essential aspect of mental function, but remains very poorly understood. In this paper, we take the view that thought is a response process — the emergent and dynamic configuration of structured response, i.e., ideas, by composing response elements, i.e., concepts, from a repertoire under the influence of afferent information, internal modulation and evaluative feedback. We hypothesize that the process of generating ideas occurs at two levels: 1) The identification of a context-specific subset — or workspace — of concepts from the larger repertoire; and 2) The configuration of plausible/useful ideas within this workspace. Workspace configuration is mediated by a dynamic selector network (DSN), which is an internal attention/working memory system. Each unit of the DSN selectively gates a subset of concepts, so that any pattern of activity in the DSN defines a workspace. The configuration of efficient and flexible workspaces is mediated by dynamical structures termed graded attractors — attractors where the set of active units can be varied in systematic order by inhibitory modulation. A graded attractor in the DSN can project a selective bias — a ``searchlight" — onto the concept repertoire to define a specific workspace, and inhibitory modulation can be used to vary the breadth of this workspace. As it experiences various contexts, the cognitive system can configure a set of graded attractors, each covering a domain of similar contexts. In this paper, we focus on a mechanism for configuring context-specific graded attractors, and evaluate its performance over a set of contexts with varying degrees of similarity. In particular, we look at whether contexts are clustered appropriately into a minimal number of workspaces based on the similarity of the responses they require. While the focus in this paper is on semantic workspaces, the model is broadly applicable to other cognitive response functions such as motor control or memory recall.

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ that is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory.

We argue that transfer of spatial and conceptual knowledge between tasks and domains is an essential benchmark for multi-representational architectures aimed at human-level intelligence. The underlying hypothesis is that spatial relationships provide a natural level of abstraction, highlighting the similarities and differences between situations and domains. Therefore, not only will spatial representations improve domain reasoning and learning, they will also facilitate the transfer of knowledge across domains. The simulated environments of real-time strategy (RTS) games provide an excellent test-bed for exploring this hypothesis for two reasons: many different RTS domains have been constructed and RTS requires a wide range of reasoning tasks.

The current "Cognitive Systems" initiative under the Euro­pean Commission's "7th Framework Programme for Re­search and Technological Development (FP7)" originated shortly after the turn of the millenium when, under the heading of "Cognitive Vision", a set of nine projects was selected for funding. Its general aim is to give a new im­petus to (1) strengthening the scientific foundation for en­gineering artificial cognitive systems - i.e., artificial sys­tems that perceive and (inter-) act, based on a suitable un­derstanding of their environment; and thus to provide the ground for (2) advancing or creating enabling technologies for a variety of applications involving interaction within all sorts of environment. These pertain to, for instance, but not exclusively, robotics, assistive technologies, and language and vision based man-machine interfaces. As of November 2009 the "Cognitive Systems, Interaction, and Robotics" portfolio comprises some 100 projects, finished and ongo­ing ones, representing almost 300 MEuro in funding. This talk addresses the background, rationale and context of the European "Cognitive Systems" initiative. It high­lights key issues, current achievements and possible future directions.

Agent adaptability — the ability of agents to change behavioral strategies when it is beneficial to do so — is presumed to be an important part of the robustness of complex adaptive systems (CAS). But, determining when changing behaviors is advantageous for agents has proven quite challenging in CAS research, as sometimes behavioral change is necessary, but other times it can impose costs that exceed benefits. I present the results from experiments using an agent-based model (ABM) designed to discover thresholds after which behavioral flexibility leads to improved societal-level outcomes in groups of agents in dynamic environments. The first major result is that there are thresholds in both levels of flexibility in agent behavior and in levels of turbulence in the environment below and above which there are marked differences in utility gains for agents. In particular, relatively high flexibility leads to lower overall utility scores, as well as, surprisingly, decreased diversity and increased inequality between agents. The second result is that at very high levels of environmental turbulence, the effects of the environment alone on agent utility overshadow any benefits to agents from flexible behavior strategies. This suggests, counter-intuitively, that the best strategy for agents in very dynamic environments is simply to keep behavior constant. The third major result is that there is an interaction between agent behavior and the environment: high flexibility of other agents can effectively make an environment more "dynamic", which just fuels more flexibility, and leads to a scramble between different strategies with no utility gain. A final theoretical contribution of the paper is that the model is able to show drawbacks to flexibility without relying on costs to changing behavior, as is done in much of the literature on strategy change.

We study the dynamics and patterns of adoption of two competing technologies as well as the effectiveness and optimal- ity of viral pricing strategies by a technology seller. Our model considers two incompatible technologies of differing quality and a market in which user valuations are heterogeneous and subject to network effects. Taking the perspec- tive of a seller of the higher quality technology with imperfect information about user preferences, we investigate the problem of predicting market equilibrium outcomes. We provide partial characterization results about the structure and robustness of equilibria and give conditions under which the higher quality technology purveyor can make significant inroads into the competitor’s market share. We then show that myopic best-response dynamics in our setting are monotonic and convergent, and propose two pricing mechanisms that use this insight to help the entrant technology seller tip the market in its favor. Comparable implementations of both mechanisms reveals that the nondiscriminatory strategy, based on a calculated public price subsidy, is less costly and just as effective as a discriminatory policy. Additionally, we study discriminatory and nondiscriminatory price mechanisms in the context of profit maximization and show that problem is NP-Hard under uncertainty for both regimes. Finally, we use simulations to analyze a game in which the pricing decisions of both competing sellers are endogenous and now show, in contrast to our analytical results with exogenous prices, that a higher quality technology consistently holds a competitive advantage over the lower quality competitor, irrespective of its market share.

We have developed computer environments that support learning by teaching and the use of self regulated learning (SRL) skills through interactions with virtual agents. More specifically, students teach a computer agent, Betty, and can monitor her progress by asking her questions and getting her to take quizzes. The system provides SRL support via dialog-embedded prompts by Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning in complex science domains and facilitate development of metacognitive skills. More recently, we have also employed sequence analysis schemes and hidden Markov model (HMM) methods for assigning context to and deriving aggregated student behavior sequences from activity data. These techniques allow us to go beyond analyses of individual behaviors, instead examining how these behaviors cohere in larger patterns. We discuss the information derived from these models, and draw inferences on students’ use of self-regulated learning strategies.