Abstract

Literary novels are a mine of information stored in a narrative. Information Extraction (IE) on these novels can be directed by a specific aim to derive knowledge of interest. In this paper, we explore the problem of extracting evidence summaries for all characters in a detective novel. But in this domain, there is no standard annotated text which provides the ground truth for evidence and there exists no fixed metric of evaluation. For extracting summaries, we explore unsupervised methods of learning. We first assume that all characters are equally likely to be culprits and build evidence against each character. Given this evidence, we assess the likelihood of each character being the culprit, eventually arriving at the true culprit(s). Our contribution is the formulation of evidence summaries for characters in determining the culprit. Our experiments are based on a corpus of detective novels spanning the last hundred years. We evaluate these summaries for comprehension with the help of human readers and broadly categorize novels into three groups.

Abstract

In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of non-holonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.

Abstract

Visibility computation is critical in spatial databases for realizing various interesting and diverse applications such as defence-related surveillance, identifying interesting spots in tourist places and online warcraft games. Existing works address the problem of identifying individual locations for maximizing the visibility of a given target object. However, in case of many applications, a set of locations may be more effective than just individual locations towards maximizing the visibility of the given target object. In this paper, we introduce the Multi-Location Visibility (MLV) query. An MLV query determines the top-k query locations from which the visibility of a given target object can be maximized. We propose a portion-based transactional framework and coverage pattern mining based algorithm to process MLV queries. Our performance evaluation with real datasets demonstrates the effectiveness of the proposed scheme in terms of query processing time, pruning efficiency and target object visibility w.r.t. a recent existing scheme.

Abstract

Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.

Abstract

In this paper, we simulated a distributed, cooperative path planning technique for multiple drones (∼200) to explore an unknown region (∼10,000 connected units) in the presence of obstacles. The map of an unknown region is dynamically created based on the information obtained from sensors and other drones. The unknown area is considered a connected region made up of hexagonal unit cells. These cells are grouped to form larger cells called sub-areas. We use long range and short range communication. The short-range communication within drones in smaller proximity helps avoid re-exploration of cells already explored by companion drones located in the same subarea. The long-range communication helps drones identify next subarea to be targeted based on weighted RNN (Reverse nearest neighbor). Simulation results show that weighted RNN in a hexagonal representation makes exploration more efficient, scalable and resilient to communication failures

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r.t. the existing CMine algorithm. Index Terms—Data mining, Coverage patterns, Incremental mining, Knowledge discovery

Abstract

We present a novel robotic front-end for autonomous aerial motion-capture (mocap) in outdoor environments. In previous work, we presented an approach for cooperative detection and tracking (CDT) of a subject using multiple micro-aerial vehicles (MAVs). However, it did not ensure optimal view-point configurations of the MAVs to minimize the uncertainty in the person’s cooperatively tracked 3D position estimate. In this article, we introduce an active approach for CDT. In contrast to cooperatively tracking only the 3D positions of the person, the MAVs can actively compute optimal local motion plans, resulting in optimal view-point configurations, which minimize the uncertainty in the tracked estimate. We achieve this by decoupling the goal of active tracking into a quadratic objective and non-convex constraints corresponding to angular configurations of the MAVs w.r.t. the person. We derive this decoupling using Gaussian observation model assumptions within the CDT algorithm. We preserve convexity in optimization by embedding all the non-convex constraints, including those for dynamic obstacle avoidance, as external control inputs in the MPC dynamics. Multiple real robot experiments and comparisons involving 3 MAVs in several challenging scenarios are presented.

Abstract

In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute realtime collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. This approach facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments.

Abstract

In this work, we address traffic management of multiple payload transport systems comprising of nonholonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each PTS completes its given task by avoiding collisions with other payload systems and obstacles as well. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle with respect to the leader using a decentralized leader follower control architecture while moving in the traffic. We showcase, through simulations the time taken by each PTS to traverse its respective trajectory with and without other PTS and obstacles. We show that our strategies help manage the traffic for a large number of PTS moving from one place to another.

Abstract

This paper presents our solution for transportation of deformable payloads using a formation of non-holonomic mobile robots while avoiding static and dynamic obstacles. We assume the center of the formation to be a virtual robot which also plays the role of the leader of the formation. Rapidly exploring Random Tree star (RRT*) algorithm is used to determine a static obstacle free trajectory for the virtual leader. In addition to that, a custom path planning algorithm is incorporated for the follower robots. A decentralized leader-follower formation control is applied for the robots to traverse their respective trajectories without breaking the formation while following the constraints of the deformable payload. The virtual leader moves forward or backward on the initially generated path with variable velocity depending on the proximity of dynamic obstacles. All the other robots follow it so that the formation is able to avoid the dynamic obstacles while still remaining on the path leading towards the destination.