The recent state-of-the-art natural language understanding (NLU) systems often behave unpredictably, failing on simpler reasoning examples. Despite this, there has been limited focus on quantifying progress towards systems with more predictable behavior. We think that reasoning capability-wise behavioral summary is a step towards bridging this gap. We create a CheckList test-suite (184K examples) for the Natural Language Inference (NLI) task, a representative NLU task. We benchmark state-of-the-art NLI systems on this test-suite, which reveals fine-grained insights into the reasoning abilities of BERT and RoBERTa. Our analysis further reveals inconsistencies of the models on examples derived from the same template or distinct templates but pertaining to same reasoning capability, indicating that generalizing the models' behavior through observations made on a CheckList is non-trivial. Through an user-study, we find that users were able to utilize behavioral information to generalize much better for examples predicted from RoBERTa, compared to that of BERT.

Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many constraints in the video domain due to challenging dynamics of spatio-temporal interactions (e.g., Between which objects are there an interaction? When do relations occur and end?). To date, two representative methods have been proposed to tackle Video Visual Relation Detection (VidVRD): segment-based and window-based. We first point out the limitations these two methods have and propose Temporal Span Proposal Network (TSPN), a novel method with two advantages in terms of efficiency and effectiveness. 1) TSPN tells what to look: it sparsifies relation search space by scoring relationness (i.e., confidence score for the existence of a relation between pair of objects) of object pair. 2) TSPN tells when to look: it leverages the full video context to simultaneously predict the temporal span and categories of the entire relations. TSPN demonstrates its effectiveness by achieving new state-of-the-art by a significant margin on two VidVRD benchmarks (ImageNet-VidVDR and VidOR) while also showing lower time complexity than existing methods - in particular, twice as efficient as a popular segment-based approach.

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.

Logical frameworks and meta-languages form a common substrate for representing, implementing and reasoning about a wide variety of deductive systems of interest in logic and computer science. Their design, implementation and their use in reasoning tasks, ranging from the correctness of software to the properties of formal systems, have been the focus of considerable research over the last two decades. This workshop brings together designers, implementors and practitioners to discuss various aspects impinging on the structure and utility of logical frameworks, including the treatment of variable binding, inductive and co-inductive reasoning techniques and the expressiveness and lucidity of the reasoning process.

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction. Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task. Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome. Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity. The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956.

The divide-and-conquer method has been widely used for estimating large-scale kernel ridge regression estimates. Unfortunately, when the response variable is highly skewed, the divide-and-conquer kernel ridge regression (dacKRR) may overlook the underrepresented region and result in unacceptable results. We develop a novel response-adaptive partition strategy to overcome the limitation. In particular, we propose to allocate the replicates of some carefully identified informative observations to multiple nodes (local processors). The idea is analogous to the popular oversampling technique. Although such a technique has been widely used for addressing discrete label skewness, extending it to the dacKRR setting is nontrivial. We provide both theoretical and practical guidance on how to effectively over-sample the observations under the dacKRR setting. Furthermore, we show the proposed estimate has a smaller asymptotic mean squared error (AMSE) than that of the classical dacKRR estimate under mild conditions. Our theoretical findings are supported by both simulated and real-data analyses.

Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Members of the Problem Solvers Caucus announced on Tuesday announced their support of the $973 billion bipartisan deal announced last month by a bipartisan group of senators. The caucus, which is comprised of a bipartisan group of 58 representatives, said it "strongly supports" the Senate infrastructure framework that is "closely aligned with our own'Building Bridges' proposal." Rep. Josh Gottheimer, a co-chair from the caucus, said the group worked with colleagues in the Senate on the framework.

Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2"). Neural sequence models -- which have been increasingly successful at performing complex, structured tasks -- exhibit the advantages and failure modes of System 1: they are fast and learn patterns from data, but are often inconsistent and incoherent. In this work, we seek a lightweight, training-free means of improving existing System 1-like sequence models by adding System 2-inspired logical reasoning. We explore several variations on this theme in which candidate generations from a neural sequence model are examined for logical consistency by a symbolic reasoning module, which can either accept or reject the generations. Our approach uses neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.

Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system.