Eric Schmidt has prodded the Pentagon for years to hurry along its software-buying process. Today the AI tech investor and former Google CEO is more determined than ever to urge government decision-makers to pick up the pace, but not just when it comes to buying more software for the Defense Department. Schmidt wants the government to implement his sweeping blueprint to fight what he considers an existential threat to democracy posed by China's AI plans, an effort that could also bolster his own commercial AI interests. He says the U.S.'s national security and economic leadership are dependent upon spending billions to procure smarter software, bolster AI research, and build the country's computer science talent pool. And he says he knows better than the Pentagon itself how to remove the bureaucratic blockades preventing more agile use of AI by the government. But at the same time, Schmidt's venture capital firm Innovation Endeavors has invested in companies that have received multimillion-dollar contracts from federal agencies. Some of those investments and contracts -- reported here for the first time -- were granted between 2016 and 2021 while Schmidt chaired two influential government initiatives, the Pentagon's Defense Innovation Board and the National Security Commission on Artificial Intelligence.

Current deep learning (DL) systems rely on a centralized computing paradigm which limits the amount of available training data, increases system latency, and adds privacy and security constraints. On-device learning, enabled by decentralized and distributed training of DL models over peer-to-peer wirelessly connected edge devices, not only alleviate the above limitations but also enable next-gen applications that need DL models to continuously interact and learn from their environment. However, this necessitates the development of novel training algorithms that train DL models over time-varying and directed peer-to-peer graph structures while minimizing the amount of communication between the devices and also being resilient to non-IID data distributions. In this work we propose, Sparse-Push, a communication efficient decentralized distributed training algorithm that supports training over peer-to-peer, directed, and time-varying graph topologies. The proposed algorithm enables 466x reduction in communication with only 1% degradation in performance when training various DL models such as ResNet-20 and VGG11 over the CIFAR-10 dataset. Further, we demonstrate how communication compression can lead to significant performance degradation in-case of non-IID datasets, and propose Skew-Compensated Sparse Push algorithm that recovers this performance drop while maintaining similar levels of communication compression.

In this paper, the multiple operating room (OR) surgical case sequencing problem (SCSP) is addressed. The objective is to maximise total OR utilisation during standard opening hours. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. Constructive heuristics are presented based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that the approach presented in this paper can be used to maintain schedule feasibility in real-time, whilst increasing OT utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and recommendations have been provided regarding implementation of the approach.

We consider a scenario where a user must make a set of correlated decisions and we propose a computational modeling of the deliberation process. We assume the user compactly expresses her preferences via soft constraints. We consider a sequential procedure that uses Decision Field Theory to model the decision making on each variable. We test this procedure on randomly generated tree-shaped Fuzzy Constraint Satisfaction Problems. Our preliminary results showed that the time increases almost in the number of nodes. This is promising in terms of modeling decision over exponentially large domains. In the future, we plan to compare our results non-sequential approach and with behavioral data to asses our approach both in terms of modeling human decision making over complex domains, and adopting DFT as a means of incorporating a form of uncertainty into the soft constraint formalism.

Constraint Programming A Constraint Satisfaction Problem (CSP) [6] consists of a set of decision variables, each with a finite domain of values, and a set of constraints specifying allowed combinations of values for some variables. A solution to a CSP is an assignment of variables to values in their respective domains such that all of the constraints are satisfied. Constraint solvers typically explore partial assignments enforcing a local consistency property. A constraint c is generalized arc consistent (GAC) if and only if when a variable is assigned any of the values in its domain, there exist compatible values in the domains of all the other variables of c. In order to enforce a local consistency property on a constraint c during search, we employ filtering algorithms that remove inconsistent values from the domains of the variables of c. These filtering algorithms are repeatedly called until no more values are pruned. This process is called constraint propagation.

Among the languages used for representing goals, actions and their consequences on the world for decision making and planning, GDL (Game Description Language) has the ability to represent complex actions in potentially uncertain and competitive environments. The aim of this paper is to exploit stochastic constraint networks in order to provide compact representations of strategic games, and to identify optimal policies in those games with generic forward checking method. From this perspective, we develop a compiler allowing to translate games, described in GDL, into instances of the Stochastic Constraint Optimization Problem (SCSP). Our compiler is proved correct for the class GDL of games with complete information and oblivious environment. The interest of our approach is illustrated by solving several GDL games with a SCSP solver.

The constraint satisfaction problem (CSP) is a computational problem in which one is to decide, given a set of constraints on variables, whether or not there is an assignment to the variables satisfying all of the constraints. This problem appears in many guises throughout computer science, for instance, in database theory, artificial intelligence, and the study of graph homomorphisms. One obtains a rich and natural family of problems by defining, for each relational structure B, the problem CSP(B) to be the case of the CSP where the relations used to specify constraints must come fromB. An increasing literature studies the algorithmic and complexity behavior of this problem family, focusing on finite and finite-like structures [1, 12, 2]; a primary research issue is to determine which such problems are polynomial-time tractable, and which are not. To this end of classifying problems, a so-called algebraic approach has been quite fruitful [5]. In short, this approach is founded on the facts that the complexity of a problem CSP(B) depends (up to polynomial-time reducibility) only on the set of relations that are primitive positive definable from B, and that this set of relations can be derived from the clone of polymorphisms ofB. Hence, the project of classifying all relational structures according to the complexity of CSP(B) can be formulated as a classification question on clones; 1 this permits the employment of algebraic notions and techniques in this project.

We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and (α,ϑ)-solutions. Instead of explicitly enumerating a very large or infinite set of future scenarios, we employ statistical estimation to determine if a given assignment is consistent for a SCSP. As in statistical estimation, the quality of our estimate is determined via confidence interval analysis. In contrast to existing approaches based on sampling, we provide likelihood guarantees for the quality of the solutions found. Our approach can be used in concert with existing strategies for solving SCSPs.

We consider scenarios where several agents must aggregate their preferences over a large set of candidates with a combinatorial structure. That is, each candidate is an element of the Cartesian product of the domains of some variables. We assume agents compactly express their preferences over the candidates via soft constraints. We consider a sequential procedure that chooses one candidate by asking the agents to vote on one variable at a time. While some properties of this procedure have been already studied, here we focus on independence of irrelevant alternatives, non-dictatorship, and strategy-proofness. Also, we perform an experimental study that shows that the proposed sequential procedure yields a considerable saving in time with respect to a non-sequential approach, while the winners satisfy the agents just as well, independently of the variable ordering and of the presence of coalitions of agents.

Many researchers in artificial intelligence are beginning to explore the use of soft constraints to express a set of (possibly conflicting) problem requirements. A soft constraint is a function defined on a collection of variables which associates some measure of desirability with each possible combination of values for those variables. However, the crucial question of the computational complexity of finding the optimal solution to a collection of soft constraints has so far received very little attention. In this paper we identify a class of soft binary constraints for which the problem of finding the optimal solution is tractable. In other words, we show that for any given set of such constraints, there exists a polynomial time algorithm to determine the assignment having the best overall combined measure of desirability. This tractable class includes many commonly-occurring soft constraints, such as 'as near as possible' or 'as soon as possible after', as well as crisp constraints such as 'greater than'. Finally, we show that this tractable class is maximal, in the sense that adding any other form of soft binary constraint which is not in the class gives rise to a class of problems which is NP-hard.