We propose a version of WalkSAT algorithm, named as BetaWalkSAT. This method uses probabilistic reasoning for biasing the starting state of the local search algorithm. Beta distribution is used to model the belief over boolean values of the literals. Our results suggest that, the proposed BetaWalkSAT algorithm can outperform other uninformed local search approaches for complex boolean satisfiability problems.

Machine learning has seen tremendous advances in the past few years which has lead to deep learning models being deployed in varied applications of day-to-day life. Attacks on such models using perturbations, particularly in real-life scenarios, pose a serious challenge to their applicability, pushing research into the direction which aims to enhance the robustness of these models. After the introduction of these perturbations by Szegedy et al., significant amount of research has focused on the reliability of such models, primarily in two aspects - white-box, where the adversary has access to the targeted model and related parameters; and the black-box, which resembles a real-life scenario with the adversary having almost no knowledge of the model to be attacked. We propose to attract attention on the latter scenario and thus, present a comprehensive comparative study among the different adversarial black-box attack approaches proposed till date. The second half of this literature survey focuses on the defense techniques. This is the first study, to the best of our knowledge, that specifically focuses on the black-box setting to motivate future work on the same.

One of the most difficult tasks in higher-order theorem proving is the instantiation of set variables [ 2, 3 ]. An important class of theorem proving problems requiring instantia tion of a set variable are those requiring induction [ 5 ]. Instantiating a set variable often requires synthesizing a formula satisfying some properties. In our work we apply machine le arning to the task of synthesizing formulas satisfying a collection of semantic properties . Previous work applying machine learning to induction theorem proving can be found in [ 7 ].

Search is a key part of many AI problem-solving strategies that assist in exploring the problem spaces. As explained by Peter Norvig "Normal programming is telling the computer what to do when we know what to do. AI is when we tell the computer what to do when we don't know what to do." Search in AI provides as a first step to start solving many of the problems, either it is finding a sequence of next steps to perform by an agent or deciding on the next game move. Many search strategies are used in AI for efficient and goal-oriented searches. Let's have a look at a few popular AI search strategies: Breadth-First Search (BFS) is the most basic searches and it finds the shortest path with respect to a number of steps between the start and goal. Simple BFS does not consider any edge costs or weights.

GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to simultaneously train differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS trains an entire grouped convolution architecture search-space concurrently. We demonstrate this through proof-of-concept architecture searches with performance objectives. GroSS represents a significant step towards liberating network architecture search from the burden of training and fine-tuning. Generally, these methods have usually involved careful network design, often relying on domain knowledge to design a structure which can encapsulate the task at hand. Neural Architecture Search (NAS) has provided an alternative to hand designed networks, allowing for the search and even direct optimisation of the network's structure. But, the search space for architectures is often vast, with potentially limitless design choices. Furthermore, each configuration must undergo some training or fine-tuning for its efficacy to be determined.

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

We propose the Square Attack, a new score-based black-box $l_2$ and $l_\infty$ adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where we select localized square-shaped updates at random positions so that the $l_\infty$- or $l_2$-norm of the perturbation is approximately equal to the maximal budget at each step. Our method is algorithmically transparent, robust to the choice of hyperparameters, and is significantly more query efficient compared to the more complex state-of-the-art methods. In particular, on ImageNet we improve the average query efficiency for various deep networks by a factor of at least $2$ and up to $7$ compared to the recent state-of-the-art $l_\infty$-attack of Meunier et al. while having a higher success rate. The Square Attack can even be competitive to gradient-based white-box attacks in terms of success rate. Moreover, we show its utility by breaking a recently proposed defense based on randomization. The code of our attack is available at https://github.com/max-andr/square-attack

The performance of acquisition functions for Bayesian optimisation is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement and the Upper Confidence Bound always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is never guaranteed to do so and Weighted Expected Improvement does only for a restricted range of weights. We introduce two novel $\epsilon$-greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that $\epsilon$-greedy algorithms are generally at least as effective as conventional acquisition functions, particularly with a limited budget. In higher dimensions $\epsilon$-greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem.

Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems,such as quadcopters.This belief that Model-based strategies that involve the use of well-trained neural networks for making such high-level decisions always give better performance can be dispelled by making use of Model-free policy search methods.This paper proposes the use of a model-free random searching strategy,called Augmented Random Search(ARS),which is a better and faster approach of linear policy training for continuous control tasks like controlling a Quadcopters flight.The method achieves state-of-the-art accuracy by eliminating the use of too much data for the training of neural networks that are present in the previous approaches to the task of Quadcopter control.The paper also highlights the performance results of the searching strategy used for this task in a strategically designed task environment with the help of simulations.Reward collection performance over 1000 episodes and agents behavior in flight for augmented random search is compared with that of the behavior for reinforcement learning state-of-the-art algorithm,called Deep Deterministic policy gradient(DDPG).Our simulations and results manifest that a high variability in performance is observed in commonly used strategies for sample efficiency of such tasks but the built policy network of ARS-Quad can react relatively accurately to step response providing a better performing alternative to reinforcement learning strategies.

Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first search and remain fixed throughout the process of solving a planning problem. Existing work combining multiple search techniques usually aims at supporting best-first search with an additional exploratory mechanism, triggered using a handcrafted criterion. A notable exception is very recent work which combines various search techniques using a trainable policy. It is, however, confined to a discrete action space comprising several fixed subroutines. In this paper, we introduce a parametrized search algorithm template which combines various search techniques within a single routine. The template's parameter space defines an infinite space of search algorithms, including, among others, BFS, local and random search. We further introduce a neural architecture for designating the values of the search parameters given the state of the search. This enables expressing neural search policies that change the values of the parameters as the search progresses. The policies can be learned automatically, with the objective of maximizing the planner's performance on a given distribution of planning problems. We consider a training setting based on a stochastic optimization algorithm known as the cross-entropy method (CEM). Experimental evaluation of our approach shows that it is capable of finding effective distribution-specific search policies, outperforming the relevant baselines.