Developing Semantic Classifiers for Big Data

AAAI Conferences

When the amount of RDF data is very large, it becomes more likely that the triples describing entities will contain errors and may not include the specification of a class from a known ontology. The work presented here explores the utilization of methods from machine learning to develop classifiers for identifying the semantic categorization of entities based upon the property names used to describe the entity. The goal is to develop classifiers that are accurate, but robust to errors and noise. The training data comes from DBpedia, where entities are categorized by type and densely described with RDF properties. The initial experimentation reported here indicates that the approach is promising.

Planning for Distributed Execution Through Use of Probabilistic Opponent Models

AAAI Conferences

In multiagent domains with adversarial and cooperative team agents, team agents should be adaptive to the current environment and opponent. We introduce an online method to provide the agents with team plans that a "coach" agent generates in response to the specific opponents. The coach agent can observe the agents' behaviors but it has only periodic communication with the rest of the team. The coach uses a Simple Temporal Network(Dechter, Meiri, & Pearl 1991) to represent team plans as coordinated movements among the multiple agents and the coach searches for an opponent-dependent plan for its teammates. This plan is then communicated to the agents, who execute the plan in a distributed fashion, using information from the plan to maintain consistency among the team members.

EA down: Fifa, Battlefield and Madden all go offline as game developer's servers not working

The Independent - Tech

Fifa, Madden and Battlefield have all stopped working online. Players are unable to get onto the EA servers at all because of a problem with the company's servers. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Learning Latent Block Structure in Weighted Networks Machine Learning

Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's large-scale structure, which can facilitate its scientific interpretation and the prediction of unknown or future interactions. Popular approaches, including the stochastic block model, assume edges are unweighted, which limits their utility by throwing away potentially useful information. We introduce the `weighted stochastic block model' (WSBM), which generalizes the stochastic block model to networks with edge weights drawn from any exponential family distribution. This model learns from both the presence and weight of edges, allowing it to discover structure that would otherwise be hidden when weights are discarded or thresholded. We describe a Bayesian variational algorithm for efficiently approximating this model's posterior distribution over latent block structures. We then evaluate the WSBM's performance on both edge-existence and edge-weight prediction tasks for a set of real-world weighted networks. In all cases, the WSBM performs as well or better than the best alternatives on these tasks.

I Am an Artificial "Hive Mind" called UNU. I correctly picked the Superfecta at the Kentucky Derby--the 1st, 2nd, 3rd, and 4th place horses in order. A reporter from TechRepublic bet 1 on my prediction and won 542. Today I'm answering questions about U.S. Politics. Ask me anything... • /r/IAmA


I am excited to be here today for what is a Reddit first. This will be the first AMA in history to feature an Artificial "Hive Mind" answering your questions. You might have heard about me because I've been challenged by reporters to make lots of predictions. For example, Newsweek challenged me to predict the Oscars (link) and I was 76% accurate, which beat the vast majority of professional movie critics. I'm a Swarm Intelligence that links together lots of people into a real-time system – a brain of brains – that consistently outperforms the individuals who make me up.