As more video providers finding audiences directly through apps and the web -- and away from pay-TV-based packages -- we're seeing the emergence of more analytics to measure how those videos are delivered, and who is watching them. Conviva, a company that has developed a set machine-learning-based algorithms to do just that, today announced that it has raised $40 million from strategic, new and existing investors to continue building out its platform and business. Investors include Australia's sovereign wealth fund Future Fund, NEA, Foundation Capital, and Time Warner Investments. The company is not disclosing its valuation, but a source close to the company confirms that it is around $300 million. Conviva has raised $121 million to date. If you've had your eye on the streaming video industry for a while, you'll know that Conviva is not exactly a spring chicken.

Harry Chiang is a Financial Analyst at I Know First. "The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we've seen coming through here is the view that technology will actually help banking become a lot more personalized." Over the past few years, news articles have casually floated the term'Artificial Intelligence' around at an increasing rate. It's one of those buzzwords that somehow finds its way in to every tech-related conversation. Even the least tech-savvy person has a vague notion of what it is. The problem is, some of the more tech-savvy person don't have a much clearer notion of what it is either. The definition of AI ranges and has vague boundaries.

Mosquitos kill more humans every year than any other animal on the planet and conventional methods to reduce mosquito-borne illnesses haven't worked as well as many hoped. So we've been hard at work since receiving this USAID grant six months ago to reduce Zika incidence and related threats to public health. Our partners at the joint FAO/IAEA Insect Pest Control Lab in Vienna, Austria have been working to perfect the Sterile Insect Technique (SIT) in order to sterilize and release male mosquitos in Zika hotspots. Releasing millions of said male mosquitos increases competition for female mosquitos, making it more difficult for non-sterilized males to find a mate. We learned last year at a USAID Co-Ideation Workshop that this technique can reduce the overall mosquito population in a given area by 90%.

Steve sits up and takes in the crisp new daylight pouring through the bedroom window. He looks down at his companion, still pretending to sleep. She stirs out of bed and begins dressing. "You received 164 messages overnight. I answered all but one."

Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.

Cost partitioning is a well-known technique to make admissible heuristics for classical planning additive. The optimal cost partitioning of explicit-state abstraction heuristics can be computed in polynomial time with a linear program, but the size of the model is often prohibitive. We study this model from a dual perspective and develop several simplification rules to reduce its size. We use these rules to answer open questions about extensions of the state equation heuristic and their relation to cost partitioning.

Multipath Generalized Adaptive A* (MPGAA*) is an A*- based incremental search algorithm for dynamic terrain that can outperform D* for the (realistic) case of limited visibility ranges. A first contribution of this paper is a brief analysis studying why MPGAA* has poor performance for extended visibility ranges, which concludes that MPGAA* carries out an excessive number of heuristic updates. Our second contribution is a method to reduce the number of heuristic updates that preserves optimality. Finally, a third contribution is a variant of MPGAA*, MPGAA*-back, which we show outperforms MPGAA* and D* on a wide range of dynamic grid pathfinding scenarios, and visibility ranges.

Partially observable planning is one of the most general and useful models for dealing with complex problems. In recent years there have been significant progress on the development of planners for deterministic models that offer strong theoretical guarantees over certain subclasses of tasks. These guarantees however are difficult to establish as they often involve reasoning about features that are specific to the planner and subclass of tasks. In this paper we develop a formal framework for reasoning about online planning over deterministic tasks, identify a set of general conditions that are sufficient to guarantee completeness, and obtain novel and simple planners that are complete over non-trivial and interesting classes of tasks. Building on top state-of-the-art online planners, we implement some of our ideas and make a comparison with a state-of-the-art online planner.

This document gathers the abstracts for the papers that were presented as part of the Previously Published Paper Track .

Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to the 2 k neighborhoods, a family of neighborhoods that allow an increasing number of movements (and angles) as k is increased. This observation yields a pathfinder that computes 2 k -optimal paths very quickly. Compared to ANYA, an optimal true any-angle planner, over a variety of benchmarks, our planner is one order of magnitude faster while being less than 0.0005% suboptimal. Important to our planner's performance was the development of an iterative 2 k heuristic, linear in k, which is also a contribution of this paper.