In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we extend BPR to adversarial settings, in particular, to opponents that switch from one stationary strategy to another. Our proposed extension enables learning new models in an online fashion when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach is capable of efficiently detecting opponent strategies and reacting quickly to behavior switches, thereby yielding better performance than state-of-the-art approaches in terms of average rewards.

Planning in hybrid systems is important for dealing with real world applications. PDDL+ supports this representation of domains with mixed discrete and continuous dynamics, and supports events and processes modeling exogenous change. Motivated by numerous SAT-based planning approaches, we propose an approach to PDDL+ planning through SMT, describing an SMT encoding that captures all the features of the PDDL+ problem as published by Fox and Long (2006). The encoding can be applied on domains with nonlinear continuous change. We apply this encoding in a simple planning algorithm, demonstrating excellent results on a set of benchmark problems.

Wildlife poaching presents a serious extinction threat to many animal species. In order to save wildlife in designated wildlife parks, park rangers conduct patrols over the park area to combat such illegal activities. An important aspect of the patrolling activity of the rangers is to anticipate where the poachers are likely to catch animals and then respond accordingly. Previous work has applied defender-attacker Stackelberg Security Games (SSGs) to solve the problem of wildlife protection, wherein attacker behavioral models are used to predict the behaviors of the poachers. However, these behavioral models have several limitations which limit their accuracy in predicting poachers' behavior. First, existing models fail to account for the rangers' imperfect observations w.r.t poaching activities (due to the limited capability of rangers to patrol thoroughly over a vast geographical area). Second, these models are built upon discrete choice models that assume a single agent choosing targets, while it is infeasible to obtain information about every single attacker in wildlife protection. Third, these models do not consider the effect of past poachers' actions on the current poachers' activities, one of the key factors affecting the poachers' behaviors. In this work, we attempt to address these limitations while providing three main contributions. First, we propose a novel hierarchical behavioral model, HiBRID, to predict the poachers' behaviors wherein the rangers' imperfect detection of poaching signs is taken into account --- a significant advance towards existing behavioral models in security games. Furthermore, HiBRID incorporates the temporal effect on the poachers' behaviors. The model also does not require a known number of attackers. Second, we provide two new heuristics: \textit{parameter separation} and \textit{target abstraction} to reduce the computational complexity in learning the model parameters. Finally, we use the real-world data collected in Queen Elizabeth National Park (QENP) in Uganda over 12 years to evaluate the prediction accuracy of our new model.

Perel told ABC News that he initially thought a bomb had been detonated outside the building, adding he was "99 percent ok, I just have a bruise." After realizing what had happened, he quickly removed the memory card from the GoPro camera attached to the DJI drone. "While sitting at my desk I heard what sounded like a missile followed by a huge bang and glass all over me," he wrote in the YouTube video description. "Turns out someone lost control of their drone.

We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). Our estimators generalize those typically used to calculate these statistics on point process data, allowing them to be calculated on RMs which assign continuous values to spatial regions, for instance to model protein intensity. The clustering index (H*) compares Ripley's H function calculated empirically to its distribution under complete spatial randomness (CSR), leading us to consider CSR null hypotheses for RMs which are not point-processes when generalizing this statistic. We thus consider restricted classes of completely random measures which can be simulated directly (Gamma processes and Marked Poisson Processes), as well as the general class of all CSR RMs, for which we derive an exact permutation-based H* estimator. We establish several properties of the estimators, including bounds on the accuracy of our general Ripley K estimator, its relationship to a previous estimator for the cross-correlation measure, and the relationship of our generalized H* estimator to previous statistics. To test the ability of our approach to identify spatial patterning, we use Fluorescent In Situ Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and protein subcellular localization patterns respectively in polarizing mouse fibroblasts on micropattened cells. We observe correlated patterns of clustering over time for corresponding mRNAs and proteins, suggesting a deterministic effect of mRNA localization on protein localization for several pairs tested, including one case in which spatial patterning at the mRNA level has not been previously demonstrated.

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model.

Do you know what is common between San Bernardino's shooting spree and the terrorist attacks in Paris last month? Jillennials, Jihadis who are Millennials. We mine data worldwide, a lot of it, a ton of it, every day and every night, and we do this for a living at PredictifyMe. We have partnership with the United Nations to protect school-goers in Pakistan, Nigeria, Sudan and Lebanon using our proprietary software SecureSim and Soothsayer . When the Paris attacks unfolded, we asked ourselves (and our database), how can we use data sciences to prevent something like this from ever happening again. Can we find out what factors influence an otherwise ordinary citizen to become radicalized?

Just yesterday, for example, Facebook Inc. (FB) rolled out a new feature, VoiceOver, that uses AI technology to give oral descriptions of FB photos for blind and visually-impaired users (iPad and iPhone users only, so far). Perhaps the big Stanley Kubrickian monolith moment for AI happened in March, with the resounding win by the Alphabet Inc. (GOOGL) AI program AlphaGo,over a champion in the complex board game Go--a game previously thought to be far too complex for a computer to play better than a human. If true believers in AI are correct that this long-promised technology is ready for the mainstream, whichever company controls AI could steer the tech industry for years to come. Hence we are witnessing a high-stakes competition to develop the next platform to establish industry dominance in upcoming product cycles. While Amazon.com Inc. (AMZN), FB, GOOGL and Microsoft Corp. (MSFT) are all jockeying furiously, the one company with the most riding on the outcome is International Business Machines Corp. (IBM).

Video Friday is your weekly selection of awesome robotics videos, collected by your fluid-filled Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. MIT has developed a 3D printer that can mix solids and liquids. With "printable hydraulics," an inkjet printer deposits individual droplets of material that are each 20 to 30 microns in diameter, or less than half the width of a human hair.

A senior Egyptian al-Qaida figure fighting in Syria was killed in a U.S. drone strike this week, the latest to be killed in such attacks in Syria, a Syrian opposition monitoring group and relatives said Friday. The Britain-based Syrian Observatory for Human Rights said Rifai Ahmad Taha was killed in a strike Tuesday in the northwestern Idlib province. Before joining al-Qaida, Taha was a top figure in Egypt's notorious militant group Gamaa Islamiya, which massacred 58 foreign tourists in the ancient Egyptian city of Luxor in 1997. He was also allied with Osama bin Laden in Afghanistan. The Observatory's chief Rami Abdurrahman said several al-Qaida members, including Taha, were killed in Tuesday's strike.