This article reviews the last two IEEE Conference on Computational Intelligence and Games (CIG) StarCraft Artificial Intelligence (AI) Competitions organized by the authors; these were the fourth and fifth in a series of annual competitions initiated in 2011. StarCraft AI Competitions have been hosted in conjunction with three different events: the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), CIG, and Student StarCraft AI Tournament (SSCAIT). The purpose of these competitions is to design bots that are able to autonomously and successfully play the StarCraft game by implementing real-time strategies. Recent results reveal the promising use of AI techniques in creating successful AI entries, but there is room for improvement with respect to the bots' ability to adapt and learn to defeat humans and scripted AI bots.

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government's newly adopted endeavor of building a "Creative Economy" and "Software Centric Society". Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

This article describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft's orbit, given their dynamic nature due to the continually updated orbit determination solution.

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty.

Civil unrest events (protests, strikes, and “occupy” events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs.

Realize this is an older thread, but need to correct the explanation from Ram, as it is not correct. I noticed this using the Wine data set available here. The resulting plot is attached. The way to interpret the plot is that each class of V1 (1, 2, or 3) corresponds to a different color in the plot. Each square is a node.

Matters of machine intelligence are topics of great interest these days. It reminds me of a statement I read a couple years back from renowned technology writer and author Kevin Kelly: "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Although that prediction proved to be a bit off the mark (or perhaps it's still too early for its time), the idea is certainly compelling. Nearly every day I hear of a new startup announcing some new intelligence offering. It's an exciting time to be in the industry.

Alpha: 'AI who beats Human Pilot in Tests' and in these lengthy tests; as usual the human subject is prone to getting tired. The Computer AI known as ALPHA does not get tired and is one of recent AI developments that reveal just how good the Artificial intelligence is getting. I have written on AI for some time now and it is evident the next stage of development that men will shoot for is AI use in many different areas. But areas where these AI robots, and instruments can work with humans as an aid. This is all fine and good until the Robot or AI instrument starts learning on it's own.