Dan McCaffrey has an ambitious goal: solving the world's looming food shortage. As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting. "By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things," McCaffrey explains. "The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer," McCaffrey adds.

The announcement of Apple's HomePod smart speaker at last week's WWDC event marks the latest entrant to a growing market for voice assistants, currently led by tech giants Amazon and Google. Each new launch brings the promise of a slicker user experience and a more efficient use of our time. It might take several iterations of these voice assistants to integrate seamlessly into our daily routines, but the first stage for any company looking to take advantage of this new tech will be to ask: How do users feel about it all? We recently partnered with Mindshare Futures and J. Walter Thompson Innovation Group for their Speak Easy research project to answer that very question. Our portion of the study involved observing 102 smartphone users as they carried out a selection of tasks using Amazon's Alexa, Google Assistant, text-based search, and questions directed to a real person.

How much information can we extract from a five-minute recording of someone talking? Enough to tell whether that individual may be genetically predisposed to some health complications, according to researchers at the University of Wisconsin–Madison's Waisman Center and Wisconsin Institute for Discovery. In a new study published this month in Scientific Reports, the researchers used machine learning to analyze hundreds of voice recordings and accurately identify individuals with a genetic condition known as fragile X premutation, which increases the risk of developing neurodegenerative disorders, infertility or having a child with fragile X syndrome. While fragile X syndrome -- characterized by intellectual disability and behavioral, physical and learning challenges -- is relatively rare, millions of people across the world have fragile X premutations. "But the premutations remain underdiagnosed, and people are often unaware of their increased health risks," says Marsha Mailick, professor of social work and UW –Madison vice chancellor for research and graduate education.

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach to approximate the likelihood in this type of models. However, such approximations can be noisy and computationally costly which hinders efficient implementations using standard methods based on optimisation and Monte Carlo methods. We propose a computationally efficient novel method based on the combination of Gaussian process optimisation and SMC-ABC to create a Laplace approximation of the intractable posterior. We exemplify the proposed algorithm for inference in stochastic volatility models with both synthetic and real-world data as well as for estimating the Value-at-Risk for two portfolios using a copula model. We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference.

Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results.

Admissible heuristics are essential for optimal planning in the context of search algorithms like A*, and they can also be used in the context of suboptimal planning in order to find quality-bounded solutions. In satisfacing planning, on the other hand, admissible heuristics are not exploited by the best-first search algorithms of existing planners even when a time window is available for improving the first solution found. For example, in the well-know planner LAMA, better solutions within such a time window are sought by restarting a Weighted-A* search guided by inadmissible heuristics, each time a better solution is found. In this paper, we investigate the use of admissible heuristics in the context of LAMA for pruning nodes that cannot lead to better solutions. The revised search of LAMA is experimentally evaluated using two alternative admissible heuristics for pruning and three types of problems: planning with soft goals, planning with action costs, and planning with both action costs and soft goals. Soft goals are compiled into hard goals following the approach of Keyder and Geffner. The empirical results show that the use of admissible heuristics in LAMA can be of great help to improve the planner performance.

The pancake puzzle is a standard benchmark domain used to test search algorithms, and the gap heuristic is the state-of-the-art heuristic function most often used in such tests. In this work, we analyze the accuracy of this heuristic and identify ways to enhance it. We begin by showing that in the worst-case, the amount that the gap heuristic underestimates the optimal cost of a pancake puzzle state can be linear in the number of pancakes in the stack. However, empirical analysis suggests that it is extremely rare that the gap heuristic underestimates the optimal cost by more than two. We then identify several simple methods that can be used to generate large sets of problems on which the gap heuristic underestimates the optimal cost by a larger amount than it typically does on random permutations. In doing so, we provide new pancake puzzle test sets that can be used to evaluate how search algorithms behave when the heuristic is inaccurate. We also formally characterize states according to the size of the heuristic plateaus around them. This characterization allows us to efficiently compute a two-step look ahead of the gap heuristic on any state, which we can use alongside a state's dual to further improve heuristic accuracy. These enhancements substantially improve the performance of an IDA*-based pancake problem solver on both the existing benchmarks and the new ones proposed in this paper.

A* is optimal among admissible unidirectional algorithms when searching with a consistent heuristic. Recently, similar optimality bounds have been established for bidirectional search but, no practical algorithm is guaranteed to always achieve this bound. In this paper we study the nature of the number of nodes that must be expanded in any front-to-end bidirectional search. We present an efficient algorithm for computing that number and show that a theoretical param-eterized generalization of MM, with the correct parameter, is the optimal front-to-end bidirectional search. We then experimentally compare various algorithms and show how far they are from optimal.

We propose techniques for efficiently determining optimal solutions to large logistics planning domain problems. We map a problem instance to a directed graph and show that no more than one vehicle per weakly connected component of the graph is needed for an optimal solution. We propose techniques for efficiently finding the vehicles which must be employed for an optimal solution. Also we develop a strong admissible heuristic based on the analysis of a directed graph, the cycles of which represent situations in the problem state in which a vehicle must visit a location more than once. To the best of our knowledge, ours is the first method that determines optimal solutions for large logistics instances (including the largest instances in the IPC 1998 and IPC 2000 problem sets).

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this research area, numerous diverse search-based techniques were developed in the past 6 years for optimally solving MAPF under the sum-of-costs objective function. In this paper we survey these techniques, while placing them into the wider context of the MAPF field of research. Finally, we provide analytical and experimental comparisons that show that no algorithm dominates all others in all circumstances. We conclude by listing important future research directions.