Decision making under uncertain conditions has been well studied when uncertainty can only be considered at the associative level of information. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rationally making choices using associative information. We provide here a previous result from Pearl and show that it can be considered as a causal version of the von Neumann-Morgenstern Theorem; furthermore, we consider the case when the true causal mechanism that controls the environment is unknown to the decision maker and propose a causal version of the Savage Theorem. As applications, we argue how previous optimal action learning methods for causal environments fit within the Causal Savage Theorem we present thus showing the utility of our result in the justification and design of learning algorithms; furthermore, we define a Causal Nash Equilibria for a strategic game in a causal environment in terms of the preferences induced by our Causal Decision Making Theorem.

Leo-III is an automated theorem prover for extensional type theory with Henkin semantics and choice. Reasoning with primitive equality is enabled by adapting paramodulation-based proof search to higher-order logic. The prover may cooperate with multiple external specialist reasoning systems such as first-order provers and SMT solvers. Leo-III is compatible with the TPTP/TSTP framework for input formats, reporting results and proofs, and standardized communication between reasoning systems, enabling e.g. proof reconstruction from within proof assistants such as Isabelle/HOL. Leo-III supports reasoning in polymorphic first-order and higher-order logic, in all normal quantified modal logics, as well as in different deontic logics. Its development had initiated the ongoing extension of the TPTP infrastructure to reasoning within non-classical logics.

Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. Deep architectures are typically trained following a supervised scheme and, therefore, they rely on the availability of a large amount of labeled training data to effectively learn their parameters. Neuro-symbolic approaches have recently gained popularity to inject prior knowledge into a deep learner without requiring it to induce this knowledge from data. These approaches can potentially learn competitive solutions with a significant reduction of the amount of supervised data. A large class of neuro-symbolic approaches is based on First-Order Logic to represent prior knowledge, that is relaxed to a differentiable form using fuzzy logic. This paper shows that the loss function expressing these neuro-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator. When restricted to simple supervised learning, the presented theoretical apparatus provides a clean justification to the popular cross-entropy loss, that has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. One advantage of the proposed learning formulation is that it can be extended to all the knowledge that can be represented by a neuro-symbolic method, and it allows the development of a novel class of loss functions, that the experimental results show to lead to faster convergence rates than other approaches previously proposed in the literature.

A common feature in Answer Set Programming is the use of a second negation, stronger than default negation and sometimes called explicit, strong or classical negation. This explicit negation is normally used in front of atoms, rather than allowing its use as a regular operator. In this paper we consider the arbitrary combination of explicit negation with nested expressions, as those defined by Lifschitz, Tang and Turner. We extend the concept of reduct for this new syntax and then prove that it can be captured by an extension of Equilibrium Logic with this second negation. We study some properties of this variant and compare to the already known combination of Equilibrium Logic with Nelson's strong negation. Under consideration for acceptance in TPLP.

Wow, educational toys have changed a lot since I was a kid. I remember inserting floppy disks (!) into a computer in order to play classic games like "Number Munchers" and "The Oregon Trail". I learned very quickly that "Dog" was not a day of the week, and that it was very easy to die of wasting diseases in the western US in the 19th century. As the world becomes more and more digitally inclined, parents and teachers alike want toys that teach kids computer-and technology-related skills, both for their future employability and for being a citizen in a society built on 1's and 0's. One emerging trend is toys that teach kids how to write computer programming code. Coding is becoming essential knowledge because the world runs on computers, and computers themselves run on code. As a person with a degree in a STEM field, I had to learn how to code later in life, and it was a miserably long learning curve (even if it's one of my favorite things to do now).

New'Wolfenstein: Youngblood' video game brings twin sisters to the forefront in the long-running alternative history battle against the Nazis. The storied video game "Wolfenstein 3D" is considered by many to have helped spark the first-person shooter genre, which spawned successors such as "Doom" and "Call of Duty." Ever since that first game, released in 1992, "B.J." Blazkowicz has been the hero of the series fighting Nazis in the game's alternate historical setting, in which Germany won World War II. But this latest game turns the action over to his twin daughters. Microsoft Xbox One, Nintendo Switch, Sony PlayStation 4, and PCs, rated Mature for ages 17-up), the young adult sisters, Jessie ("Jess") and Zofia ("Soph"), must search for their father who has gone missing.

Global supply chains are the order of the day in the auto industry, pharmaceuticals and consumer electronics to name a few. A single product could have hundreds of thousands of suppliers. Delays can result in shortages, overstocking and poor customer experiences. To procure raw materials, manage trading partners, plan sequences and execute tasks while processing huge volumes of data, is a large task, fit for data analytics using AI. AI has been used since the early 2000s to forecast demand using historical shipping daa, according to an account in DisCo (short for Disruptive Competition Project).

Imagine having a rat clinging to your back, sucking out your fat stores. That's similar to what infested bees endure when the Varroa destructor mite comes calling. Some bees fight back, wiggling, scratching, and biting until the mites depart for friendlier backs. Now, researchers, professional beekeepers, and hobbyists are working on ways to breed into bees these mite-defeating behaviors to rid them of these damaging pests. Host Sarah Crespi and Staff Writer Erik Stokstad discuss the tactics of, and the hurdles to, pesticide-free mite control.

Or at least we keep hearing it does, and we've been hearing it for centuries. And the specter never shows up. I doubt that it ever will. It's certainly possible, and improving technology will continue to creatively destroy certain sectors. To use one example, the days of long-haul truck drivers might be numbered.

AI and data science is transforming business. The CxO summit will give you the knowledge and connections to be at the forefront.