XOR-Sampling for Network Design with Correlated Stochastic Events

Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent. In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated. We use Markov Random Fields to model the correlation and define a new stochastic network design framework. We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler. The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.Comment: In Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17). The first two authors contribute equall

Intercountry Adoption Agencies and the HCIA

This report discusses concerns raised by participants of Thematic Area 3 (Intercountry Adoption Agencies and the HCIA) of the International Forum on Intercountry Adoption and Global Surrogacy held in August 2014. The aim is to report the views of those participating in this area on the issues raised by the Hague Conference (HCCH) as likely to be matters of concern at the 4th Speial Commission scheduled for June 2015. After an opening session, the Area shared sessions with 4 of the other Thematic Areas and in the reports on those joint sessions there will inevitably be some overlap with the reports from the other areas involved, but this report will seek to view issues from the perspective of the agencies and the Central Authorities responsible for accrediting them and delegating activities as allowed under the 1993 Convention. The issues discussed included the meaning of subsidiarity and the ‘best interests of the child’; the extent to which agencies in receiving States took on board the views of first parents and of the country of origin of the child; the crisis facing agencies and other accredited bodies as the number of intercountry adoptions falls while the children involved are more likely to have ‘special needs’, so that the task of selecting and preparing prospective adoptive parents - and the provision of post-adoption support - becomes more complex at a time when income is falling. This led to an exploration of the meaning of special needs and how agencies should identify such adoptions. Throughout the sessions participants examined the argument that agencies, which had been seen as a solution to the problems of independent adoptions, have become a part of the problem and at worst accused of trafficking and ‘rescue’, ignoring the principle of subsidiarity and the rights of the child and her first family. In a joint session with Thematic Area 5 the possible lessons for cross-border surrogacy from sixty years intercountry adoption were explored and the arguments for a new Hague Convention to deal with this activity examined, with a particular focus on the possibility of accrediting persons and bodies involved

A second step toward the polynomial hierarchy

AbstractSome of the questions posed by Baker et al. [1] are here answered. The principal result is that there exists a recursive oracle for which the relativized polynomial hierarchy exists through the second level; that is, there is a recursive set B such that Σ2P,B ≠ Π2P,B. It follows that Σ2P,B ⊊ Σ3P,B

Graph Value Iteration

In recent years, deep Reinforcement Learning (RL) has been successful in various combinatorial search domains, such as two-player games and scientific discovery. However, directly applying deep RL in planning domains is still challenging. One major difficulty is that without a human-crafted heuristic function, reward signals remain zero unless the learning framework discovers any solution plan. Search space becomes \emph{exponentially larger} as the minimum length of plans grows, which is a serious limitation for planning instances with a minimum plan length of hundreds to thousands of steps. Previous learning frameworks that augment graph search with deep neural networks and extra generated subgoals have achieved success in various challenging planning domains. However, generating useful subgoals requires extensive domain knowledge. We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances that are out of reach for domain-specialized solvers. In particular, instead of receiving learning signals only from discovered plans, our approach also learns from failed search attempts where no goal state has been reached. The graph value iteration component can exploit the graph structure of local search space and provide more informative learning signals. We also show how we use a curriculum strategy to smooth the learning process and perform a full analysis of how graph value iteration scales and enables learning

Global Commercial Surrogacy and International Adoption: Parallels and Differences

Over the decades, there have been numerous trends in the formation of family for those experiencing infertility. Adoption – initially domestic but now mostly international – has long been a prevailing method, with a dual outcome of also finding homes for parentless children. Those would-be parents with a stronger desire for genetic relatedness have turned to assisted reproductive technologies for the creation of their families. In the 21st century, capitalising on globalisation and advances in medical sciences and communication, global commercial surrogacy (GCS) is emerging as a dominant method of family formation. In seeking to publish this article in Adoption & Fostering, our primary objective was to provide its readership with an introductory look at GCS, thereby expanding an awareness of surrogacy to an audience whose work has traditionally been concerned with the care and protection of children through foster care and adoption. A secondary aim was to see where the long-standing field of adoption could potentially inform the burgeoning field of global commercial surrogacy. To achieve these objectives, we use international adoption and the adoption triangle as a framework, as we look at the similarities and differences between: (1) the adoptive and commissioning parents; (2) the birth mother and the surrogate; and (3) the adopted children and the children born of global surrogacy

Focused Local Search for Random 3-Satisfiability

A local search algorithm solving an NP-complete optimisation problem can be viewed as a stochastic process moving in an 'energy landscape' towards eventually finding an optimal solution. For the random 3-satisfiability problem, the heuristic of focusing the local moves on the presently unsatisfiedclauses is known to be very effective: the time to solution has been observed to grow only linearly in the number of variables, for a given clauses-to-variables ratio $\alpha$ sufficiently far below the critical satisfiability threshold $\alpha_c \approx 4.27$. We present numerical results on the behaviour of three focused local search algorithms for this problem, considering in particular the characteristics of a focused variant of the simple Metropolis dynamics. We estimate the optimal value for the ``temperature'' parameter $\eta$ for this algorithm, such that its linear-time regime extends as close to $\alpha_c$ as possible. Similar parameter optimisation is performed also for the well-known WalkSAT algorithm and for the less studied, but very well performing Focused Record-to-Record Travel method. We observe that with an appropriate choice of parameters, the linear time regime for each of these algorithms seems to extend well into ratios $\alpha > 4.2$ -- much further than has so far been generally assumed. We discuss the statistics of solution times for the algorithms, relate their performance to the process of ``whitening'', and present some conjectures on the shape of their computational phase diagrams.Comment: 20 pages, lots of figure