We present a consequence-based calculus for concept subsumption and classification in the description logic ALCHOIQ, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals. By using standard transformations, our calculus extends to SROIQ, which covers all of OWL 2 DL except for datatypes. A key feature of our calculus is its pay-as-you-go behaviour: unlike existing algorithms, our calculus is worst-case optimal for all the well-known proper fragments of ALCHOIQ, albeit not for the full logic.

Journal of Parallel and Distributed Computing 104, 19-35.

Yesterday several police departments in California announced the arrest of the "Golden State Killer," who killed a dozen people between 1978 and 1986 and has been accused of over 50 rapes. At the time, investigators said DNA played a role in identifying former Auburn, CA police officer Joseph James DeAngelo, and today went a step further with the explanation. The LA Times and New York Times report that investigators took DNA samples from the old crime scenes and plugged them into online databases, looking for familiar matches that would help narrow down the suspect. The Olympian reports that by combing through family trees of partial matches they focused on DeAngelo, who was the right age and had lived in some of the areas. Before arresting the subject they got a DNA sample from something DeAngelo had discarded and checked for a match.

"[…] we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible." Indeed the results presented by the team of Google and MIT researchers includes findings that could signal new competition for the most venerable stalwarts in the world of indexing: the B-Tree and the Hash Map. The engineering community is ever abuzz about the future of machine learning; as such the research paper has made its rounds on Hacker News, Reddit, and through the halls of engineering communities worldwide. New research is an excellent opportunity to reexamine the fundamentals of a field; and it's not often that something as fundamental (and well studied) as indexing experiences a breakthrough. This article serves as an introduction to hash tables, an abbreviated examination of what makes them fast and slow, and an intuitive view of the machine learning concepts that are being applied to indexing in the paper. In response to the findings of the Google/MIT collaboration, Peter Bailis and a team of Stanford researchers went back to the basics and warned us not to throw out our algorithms book just yet. Bailis' and his team at Stanford recreated the learned index strategy, and were able to achieve similar results without any machine learning by using a classic hash table strategy called Cuckoo Hashing. In a separate response to the Google/MIT collaboration, Thomas Neumann describes another way to achieve performance similar to the learned index strategy without abandoning the well tested and well understood B-Tree.

We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.

By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" correspond to broad distributions that encompass more specific words such as "animal" or "instrument". We introduce density order embeddings, which learn hierarchical representations through encapsulation of probability densities. In particular, we propose simple yet effective loss functions and distance metrics, as well as graph-based schemes to select negative samples to better learn hierarchical density representations. Our approach provides state-of-the-art performance on the WordNet hypernym relationship prediction task and the challenging HyperLex lexical entailment dataset -- while retaining a rich and interpretable density representation.

In this paper we discuss some of the issues concerning the Memory and Content aspects in the recent debate on the identification of a Standard Model of the Mind (Laird, Lebiere, and Rosenbloom in press). In particular, we focus on the representational models concerning the Declarative Memories of current Cognitive Architectures (CAs). In doing so we outline some of the main problems affecting the current CAs and suggest that the Conceptual Spaces, a representational framework developed by Gardenfors, is worth-considering to address such problems. Finally, we briefly analyze the alternative representational assumptions employed in the three CAs constituting the current baseline for the Standard Model (i.e. SOAR, ACT-R and Sigma). In doing so, we point out the respective differences and discuss their implications in the light of the analyzed problems.

This weakness could be the reason that many previous works that learn dynamics models of RL environments but don't actually use those models to fully replace the actual environments . Like in the M model proposed in, the dynamics model is a deterministic differentiable model, making the model easily exploitable by the agent if it is not perfect. Using Bayesian models, as in PILCO, helps to address this issue with the uncertainty estimates to some extent, however, they do not fully solve the problem. Recent work combines the model-based approach with traditional model-free RL training by first initializing the policy network with the learned policy, but must subsequently rely on a model-free method to fine-tune this policy in the actual environment.In Learning to Think, it is acceptable that the RNN M isn't always a reliable predictor. A (potentially evolution-based) RNN C can in principle learn to ignore a flawed M, or exploit certain useful parts of M for arbitrary computational purposes including hierarchical planning etc.

Recent work in natural language processing has looked at learning text to code translation models using parallel pairs of text and code samples from example source code libraries (for a review, see Neubig (2016)). In particular, Richardson and Kuhn (2017a,b); Richardson et al. (2018) look at learning to translate short text descriptions to function signature representations as a first step towards modeling the semantics of function documentation. Examples pairs of docstring and function signature representations are shown in Figure 1; using such pairs, the goal is to learn a general model that can robustly translate a given description of a function to a formal representation of that function. Initially, these datasets were proposed as a synthetic resource for studying semantic parser induction (Mooney, 2007), or for building models that learn to translate text to formal meaning representations from parallel data (see Richardson et al. (2017) for a proposal on using these datasets for the inverse problem of data-to-text generation). To date, we have built around 45 API datasets across 11 popular programming languages (e.g., Python, Java, C, Scheme, Haskell, PHP) and 7 natural languages (see Richardson (2017)), each using an ad hoc rendering of the target function signature representations. In this brief note, we define a unified syntax for expressing these representations, as well as a systematic mapping into first-order logic and a small subject domain model. In doing this, we aim to answer the following question: what do these function signatures that are being learned actually mean, and how can they be used for solving more complex natural language understanding problems (for a similar idea, see Bos (2016))? By recasting the learned representations in terms of classical logic, the hope is that our datasets will in particular be made more accessible to studies on natural language based program synthesis (Raza et al., 2015) and natural language programming more generally. In what follows, we first define a general syntax for these representations, then discuss the mapping into logic and the various applications that motivate our particular approach and subject domain model.

This course is a go-to guide for the four topics, logic programming, heuristic search, genetic algorithms and building games with AI. It will help you learn to programme with AI. The course will start with the basic puzzles, parsing trees and expression matching. This will be followed by building solutions for region coloring and maze solving. The course also has fun-filled videos on building bots to play Tic-tac-toe, Connect Four and Hexapawn.