When describing routes not in the current environment, a common strategy is to anchor the description in configurations of salient landmarks, complementing the verbal descriptions by "placing" the non-visible landmarks in the gesture space.  Understanding such multimodal descriptions and later locating the landmarks from real world is a challenging task for the hearer, who must interpret speech and gestures in parallel, fuse information from both modalities, build a mental representation of the description, and ground the knowledge to real world landmarks.  In this paper, we model the hearer's task, using a multimodal spatial description corpus we collected.  To reduce the variability of verbal descriptions, we simplified the setup to use simple objects as landmarks.  We describe a real-time system to  evaluate the separate and joint contribution of the modalities. We show that gestures not only help to improve the overall system performance, even if to a large extent they encode redundant information, but also result in earlier final correct interpretations. Being able to build and apply representations incrementally will be of use in more dialogical settings, we argue, where it can enable immediate clarification in cases of mismatch.

Human action recognition remains a difficult problem for AI. Traditional machine learning techniques can have high recognition accuracy, but they are typically black boxes whose internal models are not inspectable and whose results are not explainable. This paper describes a new pipeline for recognizing human actions from skeleton data via analogical generalization. Specifically, starting with Kinect data, we segment each human action by temporal regions where the motion is qualitatively uniform, creating a sketch graph that provides a form of qualitative representation of the behavior that is easy to visualize. Models are learned from sketch graphs via analogical generalization, which are then used for classification via analogical retrieval. The retrieval process also produces links between the new example and components of the model that provide explanations. To improve recognition accuracy, we implement dynamic feature selection to pick reasonable relational features. We show the explanation advantage of our approach by example, and results on three public datasets illustrate its utility.

The Leiter International Performance Scale-Revised (Leiter-R) is a standardized cognitive test that seeks to "provide a nonverbal measure of general intelligence by sampling a wide variety of functions from memory to nonverbal reasoning." Understanding the computational building blocks of nonverbal cognition, as measured by the Leiter-R, is an important step towards understanding human nonverbal cognition, especially with respect to typical and atypical trajectories of child development. One subtest of the Leiter-R, Form Completion, involves synthesizing and localizing a visual figure from its constituent slices. Form Completion poses an interesting nonverbal problem that seems to combine several aspects of visual memory, mental rotation, and visual search. We describe a new computational cognitive model that addresses Form Completion using a novel, mental-rotation-friendly image representation that we call the Polar Augmented Resolution (PolAR) Picture, which enables high-fidelity mental rotation operations. We present preliminary results using actual Leiter-R test items and discuss directions for future work.

For years, marketers have talked about brands as having personalities. Now they have the tools to bring those brands to life – virtually at least. Rapid developments in artificial intelligence (AI) are being combined with Academy Award-winning animation skills to create virtual humans that are the closest yet to flesh and blood. And for brands, that offers the opportunity to put a very human-looking face on a corporate body. One of the latest iterations of these virtual humans comes from Auckland-based company, Soul Machines, whose co-founder and CEO, Mark Sagar's ground-breaking work in computer-generated faces on films, King Kong and Avatar, was recognised with consecutive Oscars.

Should Artificial Intelligence strive to model and understand human cognitive and perceptual systems? Should it operate at a more abstract mathematical level of characterizing possible intelligent action, independent of human performance? Or, should it focus on building working programs that exhibit increasingly expert behavior, irrespective of theoretical or psychological conccrlls? These questions lie at the heart of most current, debate on whether AI is a science, an art, or a new branch of engineering In fact, some researchers believe it is all three and consequently build systems that perform some interesting task, arguing for the "theoretical significance" and "psychological validity" of the approach. In fact, it assumes the cognitive psychology paradigm as central and suggests that AI research would benefit from closer adherence to the data and methods of psychological research We welcome contributions in support of other research methodologies in AI, as well as discussions com-Rcscarch for this paper was conducted at the LJniversity of Chicago Center for Cognitive Science under a grant.

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

It is 1955, and in the corridors of RAND (Research and Development) Corporation, America's non-profit global policy think-tank, a printer is printing out a map using punctuation marks and symbols. Maybe, but it was also the moment that inspired the development of a phenomenon that is touted be the fundamental determinant of future societies - Artificial Intelligence. Herbert A. Simon, a political scientist, Allen Newell, a researcher in computer science and cognitive psychology and Cliff Shaw, a programmer par excellence, came together after that fateful moment of observing the printer. Simon realized a machine's manipulative capabilities that could simulate decision making, akin to the process of human thought. Thus began their journey to create the Logic Theorist, a program engineered to mimic the problem-solving skills of a human being which are also revered as'the first artificial intelligence program.' Cut to 2017 and AI seems to be the only thing everyone is talking about.

Telltale's original Walking Dead game was special, blending a gut-wrenching storyline with interesting, believable characters. Five years and two seasons later (four if you count 400 Days and Michonne) the adventure has started to show its age. So for The Walking Dead Collection -- a new bundle that launches on December 5th -- the developer has given everything a visual upgrade. To explain the changes, Telltale has released a video comparing the two versions during a pivotal scene -- Lee and Clementine's first meeting. At first, the differences might seem small.

As climate change continues to cause a reduction in Arctic sea ice and overall ice cover in the polar region, the already threatened polar bears are beginning to display highly unusual behavior. Largely solitary animals in their adult life, dozens of them were seen together recently on an island in northeast Russia. A tourist boat passing by Wrangel Island, off the coast of Chukotka in Russia's Far East, saw over 200 polar bears on a mountain slope on the island. Dozens of the animals were seen at the bottom of the slope, eating the carcass of a bowhead whale that had washed ashore. The incident took place in September, but wasn't widely reported at the time.

We believe that a Standard Model of the Mind should take into account continuous state representations, continuous timing, continuous actions, continuous learning, and parallel control loops. For each of these, we describe initial models that we have made exploring these directions. While we have demonstrated that it is possible to construct high-level cognitive models with these features (which are uncommon in most cognitive modeling approaches), there are many theoretical challenges still to be faced to allow these features to interact in useful ways and to characterize what may be gained by including these features.