As the complexity and abundance of city data increases, reusable semantic models that can integrate heterogeneous data sources in a lightweight manner enable a holistic view of the city data, which is key to Urban Ecology. Our multi-disciplinary team has built an ontology for Urban Ecology that not only captures a field-validated urban model and certification process, but also explores the reuse of semantic models and their interaction with domain experts.

List of organizers of the Cognitive Computing for Augmented Human Intelligence AAAI-14 Workshop.

We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.

Diagnosis, or the process of identifying the nature and cause of an anomaly in an ontology, has been largely studied by the Semantic Web community. In the context of ontology stream, diagnosis results are not captured by a unique fixed ontology but numerous time-evolving ontologies. Thus any anomaly can be diagnosed by a large number of different explana- tions depending on the version and evolution of the ontology. We address the problems of identifying, representing, exploiting and exploring the evolution of diagnoses representations. Our approach consists in a graph-based representation, which aims at (i) efficiently organizing and linking time-evolving di- agnoses and (ii) being used for scalable exploration. The ex- periments have shown scalable diagnoses exploration in the context of real and live data from Dublin City.

Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X = x is a cause of Y = y is NP-complete in binary models (where all variables can take on only two values) and \Sigma^P_2-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family D_k^P , k = 1,2,3,..., of complexity classes is introduced, which generalizes the class D^P introduced by Papadimitriou and Yannakakis (DP is just D^P_1). We show that the complexity of computing causality under the updated definition is D^P_2 -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.

In this paper we computationally examine how subjective experience may help or harm the decision maker's learning under uncertain outcomes, frames and their interactions. To model subjective experience, we propose the "experienced-utility function" based on a prospect theory (PT)-based parameterized subjective value function. Our analysis and simulations of two-armed bandit tasks present that the task domain (underlying outcome distributions) and framing (reference point selection) influence experienced utilities and in turn, the "subjective discriminability" of choices under uncertainty. Experiments demonstrate that subjective discriminability improves on objective discriminability by the use of the experienced-utility function with appropriate framing for a given task domain, and that bigger subjective discriminability leads to more optimal decisions in learning under uncertainty.

Multiagent path planning is important in a variety of fields, ranging from games to robotics and warehouse management. Although centralized control in the joint action space can provide optimal plans, this often is computationally infeasi- ble. Decoupled planning is much more scalable. Traditional decoupled approaches perform a unit-centric decomposition, replacing a multi-agent search with a series of single-agent searches, one for each mobile unit. We introduce an orthogonal, significantly different approach, following a spatial distribution that partitions a map into high- contention, bottleneck areas and low-contention areas. Lo- cal agents called controllers are in charge with one local area each, routing mobile units in their corresponding area. Dis- tributing the knowledge across the map, each controller can observe only the state of its own area. Adjacent controllers can communicate to negotiate the transfer of mobile units. We evaluate our implemented algorithm, SDP, on real game maps with a mixture of larger areas and narrow, bottleneck gateways. The results demonstrate that spatially distributed planning can have substantial benefits in terms of makespan quality and computation speed.

This paper proposes a novel algorithm for manifold alignment preserving global geometry. This approach constructs mapping functions that project data instances from different input domains to a new lower-dimensional space, simultaneously matching the instances in correspondence and preserving global distances between instances within the original domains. In contrast to previous approaches, which are largely based on preserving local geometry, the proposed approach is suited to applications where the global manifold geometry needs to be respected. We evaluate the effectiveness of our algorithm for transfer learning in two real-world cross-lingual information retrieval tasks.

Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds. The proposed approaches are based on the diffusion wavelets framework, data driven, and able to directly process directional neighborhood relationships without ad-hoc symmetrization. The proposed multiscale algorithms are evaluated using both synthetic and real-world data sets, and shown to outperform previous manifold learning methods.

In this paper, we consider the problem of jointly learning hierarchies over a set of sources and entities based on their containment relationship. We model the concept of hierarchy using a set of latent binary features and propose a generative model that assigns those latent features to sources and entities in order to maximize the probability of the observed containment. To avoid fixing the number of features beforehand, we consider a non-parametric approach based on the Indian Buffet Process. The hierarchies produced by our algorithm can be used for completing missing associations and discovering structural bindings in the data. Using simulated and real datasets we provide empirical evidence of the effectiveness of the proposed approach in comparison to the existing hierarchy agnostic approaches.