Geeth de Mel

Matching sensors to missions using a knowledge-based approach

Making decisions on how best to utilise limited intelligence, surveillance and reconnaisance (ISR) resources is a key issue in mission planning. This requires judgements about which kinds of available sensors are more or less appropriate for specific ISR tasks in a mission. A methodological approach to addressing this kind of decision problem in the military context is the Missions and Means Framework (MMF), which provides a structured way to analyse a mission in terms of tasks, and assess the effectiveness of various means for accomplishing those tasks. Moreover, the problem can be defined as knowledge-based matchmaking: matching the ISR requirements of tasks to the ISR-providing capabilities of available sensors. In this paper we show how the MMF can be represented formally as an ontology (that is, a specification of a conceptualisation); we also represent knowledge about ISR requirements and sensors, and then use automated reasoning to solve the matchmaking problem. We adopt the Semantic Web approach and the Web Ontology Language (OWL), allowing us to import elements of existing sensor knowledge bases. Our core ontologies use the description logic subset of OWL, providing efficient reasoning. We describe a prototype tool as a proof-of-concept for our approach. We discuss the various kinds of possible sensor-mission matches, both exact and inexact, and how the tool helps mission planners consider alternative choices of sensors.

Controlled English to facilitate human/machine analytical processing

Controlled English is a human-readable information representation format that is implemented using a restricted subset of the English language, but which is unambiguous and directly accessible by simple machine processes. We have been researching the capabilities of CE in a number of contexts, and exploring the degree to which a flexible and more human-friendly information representation format could aid the intelligence analyst in a multi-agent collaborative operational environment; especially in cases where the agents are a mixture of other human users and machine processes aimed at assisting the human users. CE itself is built upon a formal logic basis, but allows users to easily specify models for a domain of interest in a human-friendly language. In our research we have been developing an experimental component known as the “CE Store” in which CE information can be quickly and flexibly processed and shared between human and machine agents. The CE Store environment contains a number of specialized machine agents for common processing tasks and also supports execution of logical inference rules that can be defined in the same CE language. This paper outlines the basic architecture of this approach, discusses some of the example machine agents that have been developed, and provides some typical examples of the CE language and the way in which it has been used to support complex analytical tasks on synthetic data sources. We highlight the fusion of human and machine processing supported through the use of the CE language and CE Store environment, and show this environment with examples of highly dynamic extensions to the model(s) and integration between different user-defined models in a collaborative setting.

Ontological logic programming

In this paper, we propose Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. The proposed approach enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programs. The interpretation of these terms are delegated to an ontology reasoner during the interpretation of the program. Unlike similar approaches, OLP makes use of the full capacity of both the ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. Furthermore, using a case-study, we demonstrate the usefulness of OLP in real-life settings.

Partial observable update for subjective logic and its application for trust estimation

We proposed an approach to incorporate belief updates from partially observable evidence.We described how it can be used to estimate trustworthiness of information sources.We exploited consistency between reported and observed opinions for trust estimation.We showed that the proposed approach can be used to enhance trust estimation significantly. Subjective Logic (SL) is a type of probabilistic logic, which is suitable for reasoning about situations with uncertainty and incomplete knowledge. In recent years, SL has drawn a significant amount of attention from the multi-agent systems community as it connects beliefs and uncertainty in propositions to a rigorous statistical characterization via Dirichlet distributions. However, one serious limitation of SL is that the belief updates are done only based on completely observable evidence. This work extends SL to incorporate belief updates from partially observable evidence. Normally, the belief updates in SL presume that the current evidence for a proposition points to only one of its mutually exclusive attribute states. Instead, this work considers that the current attribute state may not be completely observable, and instead, one is only able to obtain a measurement that is statistically related to this state. In other words, the SL belief is updated based upon the likelihood that one of the attributes was observed. The paper then illustrates properties of the partial observable updates as a function of the state likelihood and illustrates the use of these likelihoods for a trust estimation application. Finally, the utility of the partial observable updates is demonstrated via various simulations including the trust estimation case.

Intelligent resource selection for sensor-task assignment : a knowledge-based approach

Sensing resources play a crucial role in the success of critical tasks such as surveillance. Therefore, it is important to assigning appropriate sensing resources to tasks such that the selected resources fully cater the needs of the tasks. However, selecting the right resources to tasks is a computationally hard problem to solve. Most of the existing approaches address the efficiency aspect of the resource selection by considering the physical aspects of the sensor network (e.g., range, power, etc.) but have ignored important domain related properties such as capabilities of assets, environmental conditions, policies and so on which makes the selection effective. In this paper we present a knowledge rich mechanism to intelligently select resources for tasks such that the selected resources sufficiently cover the needs of the tasks. Ontologies are used to capture the crucial domain knowledge and semantic matchmaking is used to perform sensor-task matching. A combination of ontological and first-order-logic reasoning is considered for the solution architecture. Keywords-Sensors; Platforms; Knowledge Representation; Reasoning; Semantic Matchmaking; Resource Assignment

Social Signal Processing for Real-Time Situational Understanding: A Vision and Approach

The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.

Service-oriented reasoning architecture for resource-task assignment in sensor networks

The net-centric ISR/ISTAR networks are expected to play a crucial role in the success of critical tasks such as base perimeter protection, border patrol and so on. To accomplish these tasks in an effective and expedient manner, it is important that these networks have the embedded capabilities to discover, delegate, and gather relevant information in a timely and robust manner. In this paper, we present a system architecture and an implementation that combines a service based reasoning mechanism with a sensor middleware infrastructure so that tasks can be executed efficiently and effectively. A knowledge base, utilising the Semantic Web technologies, provides the foundation for reasoning mechanism that assists users to discover, identify and allocate resources that are made available through the middleware, in order to satisfy the needs of tasks. Once resources are allocated to any given task, they can be accessed, controlled, shared, and their data feeds consumed through the Fabric middleware. We use the semantic descriptions from the knowledge base to annotate the resources (types, capabilities, etc.) in the sensor middleware so that they can be retrieved for reasoning during the discovery and identification phases. The reasoner is implemented as a HTTP web service, with the following characteristics: 1. Computational intensive operations are off-loaded to dedicated nodes, preserving the resources in the ISR/ISTAR networks. 2. HTTP services are accessible through a standard set of APIs irrespective of the reasoner technology used. 3. Support for seamless integration of different reasoners into the system.

A knowledge driven policy framework for internet of things

With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it manufacturing or home-care automation. However, with the explosion of IoT deployments we have observed in recent years, manually governing the interactions between humans-to-devices—and especially devices-to- devices—is an impractical task, if not an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equip to maintain a bird’s-eye-view of the interaction space. Motivated by this observation, in this paper, we propose an end-to-end framework that (a) automatically dis- covers devices, and their associated services and capabilities w.r.t. an ontology; (b) supports representation of high-level—and expressive—user policies to govern the devices and services in the environment; (c) pro- vides efficient procedures to refine and reason about policies to automate the management of interactions; and (d) delegates similar capable devices to fulfill the interactions, when conflicts occur. We then present our initial work in instrumenting the framework and discuss its details.

Agile sensor tasking for CoIST using natural language knowledge representation and reasoning

We describe a system architecture aimed at supporting Intelligence, Surveillance, and Reconnaissance (ISR) activities in a Company Intelligence Support Team (CoIST) using natural language-based knowledge representation and reasoning, and semantic matching of mission tasks to ISR assets. We illustrate an application of the architecture using a High Value Target (HVT) surveillance scenario which demonstrates semi-automated matching and assignment of appropriate ISR assets based on information coming in from existing sensors and human patrols operating in an area of interest and encountering a potential HVT vehicle. We highlight a number of key components of the system but focus mainly on the human/machine conversational interaction involving soldiers on the field providing input in natural language via spoken voice to a mobile device, which is then processed to machine-processable Controlled Natural Language (CNL) and confirmed with the soldier. The system also supports CoIST analysts obtaining real-time situation awareness on the unfolding events through fused CNL information via tools available at the Command and Control (C2). The system demonstrates various modes of operation including: automatic task assignment following inference of new high-importance information, as well as semi-automatic processing, providing the CoIST analyst with situation awareness information relevant to the area of operation.

Reasoning with uncertain information and trust

A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed approaches is shown through simulations under various settings.