This paper describes work underway at the Krasnow Institute for Advanced Study on the topic of modeling social trust. We have built and are testing an ACT-R model intended to replicate human participants building and maintaining social trust using an economic investment game. We already have behavioral and fMRI imaging data for subjects which we expect to generate comparable data by having an ACT-R model read the same inputs the humans did and decide whether to trust or not their partner.

Our central goal is to quantify the long-term progression of pediatric neurological diseases, such as a typical 10-15 years progression of child dystonia. To this purpose, quantitative models are convincing only if they can provide multi-scale details ranging from neuron spikes to limb biomechanics. The models also need to be evaluated in hyper-time, i.e. significantly faster than real-time, for producing useful predictions. We designed a platform with digital VLSI hardware for multi-scale hyper-time emulations of human motor nervous systems. The platform is constructed on a scalable, distributed array of Field Programmable Gate Array (FPGA) devices. All devices operate asynchronously with 1 millisecond time granularity, and the overall system is accelerated to 365x real-time. Each physiological component is implemented using models from well documented studies and can be flexibly modified. Thus the validity of emulation can be easily advised by neurophysiologists and clinicians. For maximizing the speed of emulation, all calculations are implemented in combinational logic instead of clocked iterative circuits. This paper presents the methodology of building FPGA modules in correspondence to components of a monosynaptic spinal loop. Results of emulated activities are shown. The paper also discusses the rationale of approximating neural circuitry by organizing neurons with sparse interconnections. In conclusion, our platform allows introducing various abnormalities into the neural emulation such that the emerging motor symptoms can be analyzed. It compels us to test the origins of childhood motor disorders and predict their long-term progressions.

Authoring narrative content for interactive digital media can be both difficult and time consuming.The research proposed here aims at enhancing the capabilities of content creators through the development of a computational model that improves the quality of automatically generated stories, potentially decreasing the burden placed on the author. The quality and believability of a story can be significantly enhanced by the presence of compelling characters. To achieve this objective, I aim to develop a choice-based computational model that facilitates the automatic generation of narrative that includes characters that are made more compelling by the presence of distinct personality characteristics.

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.