We present an approach for cyberbullying detection based on state-of-the-art text classification and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Form spring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user interaction paradigms with the goal of promoting empathy among social network participants. We propose an air traffic control-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.

Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.

In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes (POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines P-log, a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty. Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.

The Winograd Schema Challenge has recently been proposed as an alternative to the Turing test. A Winograd Schema consists of a sentence and question pair such that the answer to the question depends on the resolution of a definite pronoun in the sentence. The answer is fairly intuitive for humans but is difficult for machines because it requires commonsense knowledge about words or concepts in the sentence. In this paper we propose a novel technique which semantically parses the text, hunts for the needed commonsense knowledge and uses that knowledge to answer the given question.

We present a new set of challenge problems for the logical formalization of commonsense knowledge, called Triangle-COPA. This set of one hundred problems is smaller than other recent commonsense reasoning question sets, but is unique in that it is specifically designed to support the development of logic-based commonsense theories, via two means. First, questions and potential answers are encoded in logical form using a fixed vocabulary of predicates, eliminating the need for sophisticated natural language processing pipelines. Second, the domain of the questions is tightly constrained so as to focus formalization efforts on one area of inference, namely the commonsense reasoning that people do about human psychology. We describe the authoring methodology used to create this problem set, and our analysis of the scope of requisite commonsense knowledge. We then show an example of how problems can be solved using an implementation of weighted abduction.

The Winograd Schema Challenge is an alternative to the Turing Test that may provide a more meaningful measure of machine intelligence. It poses a set of coreference resolution problems that cannot be solved without human-like reasoning. In this paper, we take the view that the solution to such problems lies in establishing discourse coherence. Specifically, we examine two types of rhetorical relations that can be used to establish discourse coherence: positive and negative correlation. We introduce a framework for reasoning about correlation between sentences, and show how this framework can be used to justify solutions to some Winograd Schema problems.

Several recent approaches use distributional similarity for making symbolic reasoning more flexible. While an important step in the right direction, the use of similarity has a number of inherent limitations. We argue that similarity-based reasoning should be complemented with commonsense reasoning patterns such as interpolation and a fortiori inference. We show how the required background knowledge for these inference patterns can be obtained from distributional models.

This paper describes the Winograd Schema Challenge (WSC), which has been suggested as an alternative to the Turing Test and as a means of measuring progress in commonsense reasoning. A competition based on the WSC has been organized and announced to the AI research community. The WSC is of special interest to the AI applications community and we encourage its members to participate.

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.

Procedural game generation is the automatic creation of all aspects of a playable computer game. Procedural game generation systems require specialized knowledge, virtual worlds, and art assets. In this paper, we show how 3D graphical scenes for interactive fictions can be automatically generated with only knowledge that is readily available in existing knowledge bases or can be acquired via crowdsourcing. The key to 3D scene generation is commonly accepted spatial relationships between different types of objects in different types of scenes. We use a crowdsourcing game to automatically and rapidly acquire spatial relations. The spatial relations are used by an intelligent scene generation system that selects and configures 3D assets within a virtual geometric space.