Despite continuing controversies over its value in improving birth rates in IVF, testing embryos for their chromosomal content has become routine in many fertility clinics. Embryos with a normal complement of chromosomes (known as "euploid") are known to have a good chance of implanting in the uterus to become a pregnancy, while abnormal embryos (aneuploid) have no chance. Testing embryos for aneuploidy (known as PGT-A) has so far required a sample single cell or several cells taken from the embryo by biopsy, and this too has raised fears over safety such that a search for non-invasive methods has arisen in recent years. Now, a new study suggests that euploid embryos can be visually distinguished from aneuploid according to artificial intelligence references of cell activity as seen by time-lapse imaging--and thus without the need for cell biopsy. The results of the study will be presented today at the online annual meeting of ESHRE by Ms Lorena Bori from IVIRMA in Valencia, Spain, on behalf a joint research team from IVIRMA Valencia and AIVF, Israel, co-directed by Dr. Marcos Meseguer from Valencia and Dr. Daniella Gilboa from Tel-Aviv.

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.

It was founded in 1991 and, since 1994, has been located on the campus of the Autonomous University of Barcelona. IIIA grew out of an AI research group at the Center for Advanced Studies in Blanes (Spain) that started AI research in 1985. On average IIIA has had about 50 members per year during the last 12 years with a peak of almost 80 members in 2012. In total around 200 different people, including visiting researchers as well as master's and Ph.D. students, have been members of IIIA over the past 20 years. Seventy-seven students have completed their Ph.D. work at our Institute, 48 of them during the last 12 years.

ADOPT and BnB-ADOPT are two optimal DCOP search algorithms that are similar except for their search strategies: the former uses best-first search and the latter uses depth-first branch-and-bound search. In this paper, we present a new algorithm, called ADOPT( k ), that generalizes them. Its behavior depends on the k parameter. It behaves like ADOPT when k = 1, like BnB-ADOPT when k = ∞ and like a hybrid of ADOPT and BnB-ADOPT when 1 < k < ∞. We prove that ADOPT( k ) is a correct and complete algorithm and experimentally show that ADOPT( k ) outperforms ADOPT and BnB-ADOPT on several benchmarks across several metrics.

Distributed Constraint Optimization Problems (DCOPs) can be optimally solved by distributed search algorithms, such as ADOPT and BnB-ADOPT. In centralized solving, maintaining soft arc consistency during search has proved to be beneficial for performance. In this thesis we aim to explore the maintenance of different levels of soft arc consistency in distributed search when solving DCOPs.

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms like LSS-LRTA*, LRTA*(k), etc., improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding or escaping depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We apply the principle to LSS-LRTA* producing aLSS-LRTA*, a new real-time search algorithm whose search is guided towards exiting regions with heuristic depressions. We show our algorithm outperforms LSS-LRTA* in standard real-time benchmarks. In addition we prove aLSS-LRTA* has most of the good theoretical properties of LSS-LRTA*.

Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations.