Existing theoretical universal algorithmic intelligence models are not practically realizable. More pragmatic approach to artificial general intelligence is based on cognitive architectures, which are, however, non-universal in sense that they can construct and use models of the environment only from Turing-incomplete model spaces. We believe that the way to the real AGI consists in bridging the gap between these two approaches. This is possible if one considers cognitive functions as a "cognitive bias" (priors and search heuristics) that should be incorporated into the models of universal algorithmic intelligence without violating their universality. Earlier reported results suiting this approach and its overall feasibility are discussed on the example of perception, planning, knowledge representation, attention, theory of mind, language, and some others.

The Next-Generation Transportation program coordinates the evolution and transformation of the current air-traffic management (ATM) system for the National Airspace System (NAS). Currently the NAS has a limited capacity and cannot handle the increasing future air traffic demands. However, before newly proposed ATM concepts are deployed they must be rigorously evaluated under realistic conditions. This paper presents AGENTFLY, an emerging NAS-wide highfidelity multi-agent ATM simulator with precise emulation of the human controller operation workload model and human-system interaction. The simulator is validated using a flight scenario developed by the U.S. Federal Aviation Administration that is based on real data. We present preliminary results focusing on the accuracy of the simulated controllers within AGENTFLY.

Metaphors being at the heart of our language and thought process, computationally modelling them is imperative for reproducing human cognitive abilities. In this work, we propose a plausible grounded cognitive model for artificial metaphor acquisition. We put forward a rule-based metaphor acquisition system, which doesn't make use of any prior 'seed metaphor set'. Through correlation between a video and co-occurring commentaries, we show that these rules can be automatically acquired by an early learner capable of manipulating multi-modal sensory input. From these grounded linguistic concepts, we derive classes based on lexico-syntactical language properties. Based on the selectional preferences of these linguistic elements, metaphorical mappings between source and target domains are acquired.

Cognitive psychology and artificial intelligence have provided valuable insights into the scope and limitations of understanding human thought and behavior. Advances in computer technology and tools are becoming more of a fixture in everyday life, and increasingly affecting how we think about artificial intelligence and cognition. This special track is motivated by these two fronts of research. First, we extend cognitive studies to include the social psychology of people's everyday life with technology, comparing human cognition and experience with today's computer models, and second, on this basis we seek appropriate applications using computer technology, and seek to improve computer models of cognition and AI programs. This approach might yield many new ideas for creating technology and tools that amplify the ability of people to think and work together (such as new approaches for building robots in real-world domains), as well as new psychological and social theories.

Bill Swartout Introduced by Alan Kay at XEROX PARC in the 1970's, the desktop metaphor, which was later adopted in the Macintosh and Windows operating systems, has become the primary way we think about interacting with computers. Over the last decade, we have been developing sophisticated virtual humans at the USC Institute for Creative Technologies.

According to Baddeley, "The episodic buffer is assumed to be a limitedcapacity Our research team has been working on the development of a language analysis model (Ball, 2011; Ball, Heiberg & temporary storage system that is capable of Silber, 2007) within the ACT-R cognitive architecture integrating information from a variety of sources…the (Anderson, 2007) since 2002 (Ball, 2004). The focus is on buffer provides not only a mechanism for modeling the development of a general-purpose, large-scale, functional environment, but also for creating new cognitive model (Ball, 2008; Ball et al., 2010) that adheres to well representations" (ibid, p. 421). A key empirical result which established cognitive constraints on human language motivated Baddeley to introduce the episodic buffer after 25 processing (HLP) as realized by ACT-R.

Desiderata for cognitive architectures that are to support the extent of human-level intelligence required in virtual humans imply the need to bridge a range of dichotomies faced by such architectures. The focus here is first on two general approaches to building such bridges — addition and reduction — and then on a pair of general tools – graphical models and piecewise continuous functions — that exploit the second approach towards developing such an architecture. Evaluation is in terms of the architecture’s demonstrated ability and future potential for bridging the dichotomies.

Human wayfinding operates very very differently from traditional deterministic algorithms owing to a) restrictions in working memory resulting in subjective regionalized maps, and b)flexible adoption of different navigation strategies. While a number of cognitive strategies have been proposed for human wayfinding, these have been hard to evaluate thoroughly owing to a lack of computational simulation. In this work, we propose a stochastic approach for capturing these aspects, and argue for a memoryless, stationary implementation. In two longitudinal experiments on the same group of subjects, we first estimate the subjective regionalized maps for each subject on the same familiar spatial domain. Later, based on their wayfinding responses, we can estimate the stationary probabilities for different strategies. We apply this algorithm to evaluate three wayfinding strategies proposed in the literature, and repudiate the previously held suggestion that they are followed equiprobably.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum . We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

Human error is one of the most common causes of vulnerability in asecure system. However it is often overlooked when these systems aretested, partly because human tests are costly and very hard torepeat. We have developed a community of agents that test securesystems by running standard windows software while performingcollaborative group tasks, mimicking more realistic patterns ofcommunication and traffic, as well as human fatigue and errors. Thissystem is being deployed on a large cyber testing range. One keyattribute of humans is flexibility of response in order to achievetheir goals when unexpected events occur. Our agents use reactiveplanning within a BDI architecture to flexibly re-plan if needed.Since the agents are goal-oriented, we are able to measure the impactof cyber attacks on mission accomplishment, a more salient measure ofprotection than raw penetration. We show experimentally how the agentteams can be resilient under attacks that are partly successful, andalso how an organizational structure can lead to emergent propertiesof the traffic in the network.