For months now I have been in the thrall of Pokémon Trading Card Pocket. It's a devilishly slick blend of card-collecting and pared-down battling that has had me obediently opening the app on my phone at least twice a day since it launched. The virtual cards are beautifully done; the rare art cards especially, with their pastoral scenes of Pokémon in their natural habitats. I have spent many hours on the battles, too, honing decks and chasing win streaks to earn myself victory emblems. I got most of my friends into it, anticipating the day when its makers at DeNa would finally enable trading so I could fill the last couple of holes in my collection.

Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) semantic; and 3) the same RPOMDP with different assumptions leads to semantically different POSGs and, thus, different policies and values. These novel semantics for RPOMDPS give access to results for the widely studied POSG model; concretely, we show the existence of a Nash equilibrium. Finally, we classify the existing RPOMDP literature using our semantics, clarifying under which uncertainty assumptions these existing works operate.

Artificial intelligence and machine learning techniques are already proving effective in pharmaceutical procedures. Drug discovery is one of the crucial procedures to find new candidate medications in the field of medicine, biotechnology and pharmacology. According to the U.S. FDA, there are five steps for the development of a new drug. These include discovery and development, preclinical research, clinical research, FDA review, and FDA post-market safety monitoring. Since drug discovery requires huge amounts of data and research, many pharmaceutical companies are embracing AI and machine learning to accelerate the pace of drug discovery.

VMware and Nvidia have announced a pact at VMworld to make artificial intelligence (AI) a mass market. AI is entering a phase of maturity where its early adopters have discovered most of the glitches and devised a way to work around them. So virtual pioneer VMware has collaborated with graphics processing giant Nvidia (now part of Mellanox) to create a standard baseline platform on which all AI projects can be launched. Until now, the installation of AI has been far too complicated to attempt for any company that doesn't have global reach and the IT budgets to match. Companies need huge amounts of time and money to create the computing and network infrastructure on which to run all the software, host the data and process all the information, said Krish Prasad, VMware's cloud business SVP and general manager.

Behavioural change is a very tricky thing. We humans are so fickle. We see a bright shiny wearable device that can track our every move and we think it's our "silver bullet", a "ticket" to achieving our health and fitness dreams. Only for guilt to set in, as after a short time, the wearable device winds up in our top drawer. We knew the fitness data was great, but we really didn't know what to do with it. The truth is, behaviour change requires much more than data.

Behavioural change is a very tricky thing. We humans are so fickle. We see a bright shiny wearable device that can track our every move and we think it's our "silver bullet", a "ticket" to achieving our health and fitness dreams. Only for guilt to set in, as after a short time, the wearable device winds up in our top drawer. We knew the fitness data was great, but we really didn't know what to do with it. The truth is, behaviour change requires much more than data. Many programs have realized the magnitude of the problem and created incentive programs to reward people for being active, so they get a small pay-off on the road to achieving fitness. But in spite of these rewards, the drop-out rate remains problematic.

Microsoft has unveiled a new system of bots that can represent businesses and interact with users via Skype. Social bots are automated programs that can chat to users in a humanlike way. As part of the Build developer conference, Microsoft also revealed updates to digital assistant Cortana, which can interact with bots on the user's behalf. Cortana will now function across various devices and operating systems, like Android and iOS. The tech giant also announced a Skype app for its HoloLens headset.

Querying inconsistent ontologies is an intriguing new problem that gave rise to a flourishing research activity in the description logic (DL) community. The computational complexity of consistent query answering under the main DLs is rather well understood; however, little is known about existential rules. The goal of the current work is to perform an in-depth analysis of the complexity of consistent query answering under the main decidable classes of existential rules enriched with negative constraints. Our investigation focuses on one of the most prominent inconsistency-tolerant semantics, namely, the AR semantics. We establish a generic complexity result, which demonstrates the tight connection between classical and consistent query answering. This result allows us to obtain in a uniform way a relatively complete picture of the complexity of our problem.

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. Recently, the Datalog+/- family of ontology languages was introduced; in Datalog+/-, rules are tuple-generating dependencies (TGDs), i.e., Datalog rules with the possibility of having existentially-quantified variables in the head. In this paper we introduce a novel Datalog+/- language, namely sticky sets of TGDs, which allows for a wide class of joins in the body, while enjoying at the same time a low query-answering complexity. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show some extensions of sticky sets of TGDs, and how functional dependencies and so-called negative constraints can be added to a sticky set of TGDs without increasing the complexity of query answering. Our language thus properly generalizes both classical database constraints and most widespread tractable description logics.

We describe a computational model of the development and regeneration of specific eye-brain circuits. The model comprises a self-organizing map-forming network which uses local Hebb rules, constrained by (genetically determined) molecular markers. Various simulations of the development and regeneration of eye-brain maps in fish and frogs are described, in particular successful simulations of experiments by Schmidt-Cicerone-Easter; Meyer; and Y oon. 1 INTRODUCTION In a previous paper published in last years proceedings (Cowan & Friedman 1990) we outlined a new computational model for the development and regeneration of eye-brain maps. We indicated that such a model can simulate the results of a number of the more complicated surgical manipulations carried out on the visual pathways of goldfish and frogs. In this paper we describe in more detail some of these experiments, and our simulations of them.