In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

Organizing committee for the  Cognitive Robotics Workshop held at AAAI-12.

In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Data and information from people, systems and things is the single most scalable resource available to city stakeholders but difficult to publish, organize, discover and consume, especially in a real-time context. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem, could drive significant benefits and opportunities. In the context of stream data (as real-time, gigantic, noisy and private data), this paper targets research issues we identify as important to harness the fused information resources of cities, Citizens and Stakeholders to reach the concept of Smarter Cities.

In our ongoing work, we aim to control a team of agents soas to achieve a prescribed goal state while being confidentthat collisions with other agents are avoided. Each agent isassociated with a feedback controlled plant, whose continu-ous state trajectories follow some stochastic differential dy-namics. To this end we describe a collision-detection modulebased on a distribution-independent probabilistic bound andemploy a fixed priority method to resolve collisions. Dueto their practical importance, multi-agent collision avoid-ance and control have been extensively studied across differ-ent communities including AI, robotics and control. How-ever, these works typically assume linear and discrete dy-namic models; by contrast, our work intends to overcomethese limitations and to present solutions for continuousstate space. While our current experiments were conductedwith linear stochastic differential equation (SDE) modelswith state-independent noise (yielding Gaussian processes)we believe that our approach could also be applicable to non-Gaussian cases with state-dependent uncertainties.

A* is the algorithm of finding the shortest path between two nodes in a graph. When the searching problem is constituted of a set of linked graphs, A* searches solution like if it is face of one graph formed by linked graphs. While researchers have developed solutions to reduce the execution time of A* in multiple cases by multiples techniques, we develop a new algorithm: DEC-A* which is a decentralized version of A* composing a solution through a collection of graph. A* uses a distance-plus-cost heuristic function to determine the order in which the search visits nodes in the tree. Our algorithm DEC-A* extends the evaluation of the distance-plus-cost heuristic to be the sum of two functions : local distance, which evaluates the cost to reach the nearest neighbor node s to the goal, and global distance which evaluates the cost from s to the goal through other graphs. DEC-A* reduces the time of finding the shortest path and reduces the complexity, while ensuring the privacy of graphs.

This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system (Geib 2009; Geib 2011) with a world model. This is a significant increase in the expressiveness of the plan recognition system and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution. Since, ELEXIR falls in the family of gramatical methods for plan recognition in viewing the problem of plan recognition as that of parsing, this paper will also briefly discuss how this extension relates to state of the art proposals in the natural language community regarding probabilistic parsing.

Reaching for a target requires estimating the spatial position of the target and to convert such a position in a suitable arm-motor command. In the proposed framework, the location of the target is represented implicitly by the gaze direction of the robot and by the distance of the target. The NAO robot is provided with two cameras, one to look ahead and one to look down, which constitute two independent head-centered coordinate systems. These head-centered frames of reference are converted into reaching commands by two neural networks. The weights of networks are learned by moving the arm while gazing the hand, using an on-line learning algorithm that maintains the covariance matrix of weights. This work adapts a previously proposed model that worked on a full humanoid robot torso, to work with the NAO and is a step toward a more generic framework for the implicit representation of the peripersonal space in humanoid robots.

As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.

Over a series of evaluation experiments conducted using naive judges recruited and managed via Amazon's Mechanical Turk facility using a task from information retrieval (IR), we show that a SVM shows itself to have a very high accuracy when the machine-learner is trained and tested on a single task and that the method was portable from more complex tasks to simpler tasks, but not vice versa.

In an Artificial Intelligence course, a basic concept is Constraint Satisfaction (CS), which is acknowledged as a hard domain for teachers to teach and student to understand. In this paper, we present a game-based learning approach to assist students in learning CS algorithms, such as arc consistency and search algorithms, for problem solving in an easy, interactive and motivating way. Preliminary valuation has showed promising results.