A multimodal dataset of spontaneous speech and movement production on object affordances

In the longstanding effort of defining object affordances, a number of resources have been developed on objects and associated knowledge. These resources, however, have limited potential for modeling and generalization mainly due to the restricted, stimulus-bound data collection methodologies adopted. To-date, therefore, there exists no resource that truly captures object affordances in a direct, multimodal, and naturalistic way. Here, we present the first such resource of 'thinking aloud', spontaneously-generated verbal and motoric data on object affordances. This resource was developed from the reports of 124 participants divided into three behavioural experiments with visuo-tactile stimulation, which were captured audiovisually from two camera-views (frontal/profile). This methodology allowed the acquisition of approximately 95 hours of video, audio, and text data covering: object-feature-action data (e.g., perceptual features, namings, functions), Exploratory Acts (haptic manipulation for feature acquisition/verification), gestures and demonstrations for object/feature/action description, and reasoning patterns (e.g., justifications, analogies) for attributing a given characterization. The wealth and content of the data make this corpus a one-of-a-kind resource for the study and modeling of object affordances.

Reports of the AAAI 2011 Conference Workshops

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

The minimalist grammar of action

Language and action have been found to share a common neural basis and in particular a common 'syntax', an analogous hierarchical and compositional organization. While language structure analysis has led to the formulation of different grammatical formalisms and associated discriminative or generative computational models, the structure of action is still elusive and so are the related computational models. However, structuring action has important implications on action learning and generalization, in both human cognition research and computation. In this study, we present a biologically inspired generative grammar of action, which employs the structure-building operations and principles of Chomsky's Minimalist Programme as a reference model. In this grammar, action terminals combine hierarchically into temporal sequences of actions of increasing complexity; the actions are bound with the involved tools and affected objects and are governed by certain goals. We show, how the tool role and the affected-object role of an entity within an action drives the derivation of the action syntax in this grammar and controls recursion, merge and move, the latter being mechanisms that manifest themselves not only in human language, but in human action too.