Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Abstract—Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowl-edge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step towards addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Non-monotonic logical inference in ASP is used to generate a multi-nomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a Beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains. I

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype