In settings where heterogenous robotic systems interact with humans, information from the environment must be systematically captured, organized and maintained in time. In this work, we propose a model for connecting perceptual information to semantic information in a multi-agent setting. In particular, we present semantic cooperative perceptual anchoring, that captures collectively acquired pe...

ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the s...

Nowadays, knowledge graphs have become a fundamental asset for search engines which need background commonsense knowledge for natural interactions. A fair amount of user queries seek information on problem-solving tasks such as painting a wall or repairing a bicycle. While projects like ConceptNet and Webchild have successfully compiled large amounts of knowledge on properties of objects in our...

Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a di...

The area of commonsense reasoning aims at the creation of systems able to simulate the human way of rational thinking. This paper describes the use of automated reasoning methods for tackling commonsense reasoning benchmarks. For this we use a benchmark suite introduced in literature. Our goal is to use general purpose background knowledge without domain specific hand coding of axioms, such tha...

Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity generated while preserving informative content from images. We propose to foster informativeness story by using concept selection module that suggests set candidates. Then, utilize large scale pre-trained model convert concepts images into full ...

Representing objects in a dynamic domain is essential in commonsense reasoning under some circumstances. Classical logics and their nonmonotonic consequences, however, are usually not able to deal with reasoning with dynamic domains due to the fact that every constant in the logical language denotes some existing object in the static domain. In this paper, we explore a logical formalization whi...

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al. (2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and t...

In this paper we apply common sense knowledge to the task of decision making in an adventure game. We show how knowledge about the underlying domain, induced from a common sense knowledge base, can be used to do reward shaping of the environment, enabling reinforcement learning agents to learn optimal behaviours faster. The lessons learnt from this experiment could be used as a first step to fi...