Surangika Ranathunga

IDENTIFYING EVENTS TAKING PLACE IN SECOND LIFE VIRTUAL ENVIRONMENTS

Second Life is one of the most popular multi-purpose online virtual worlds, which supports applications in diversified areas relating to real-life activities. Moreover, it is possible to use Second Life in testing Artificial Intelligence theories by creating intelligent virtual agents. For the successful implementation of many of these applications, it is important to accurately identify events taking place inside Second Life. This involves extracting low-level spatio-temporal data and identifying the embedded high-level domain-specific information. This is an aspect that has not been taken into consideration in the prior research related to Second Life. This paper presents a framework that extracts data from Second Life with high accuracy and high frequency, and identifies the high-level domain-specific events and other contextual information embedded in these low-level data. This is guided by our virtual environment formalism, which defines events and states in a virtual environment. This framework is further enhanced to be connected with multiagent development platforms, thus demonstrating its use in the area of Artificial Intelligence.

Interfacing a cognitive agent platform with second life

Second Life is a popular multi-purpose online virtual world that provides a rich platform for remote human interaction. It is increasingly being used as a simulation platform to model complex human interactions in diverse areas, as well as to simulate multi-agent systems. It would therefore be beneficial to provide techniques allowing high-level agent development tools, especially cognitive agent platforms such as belief-desire-intention (BDI) programming frameworks, to be interfaced to Second Life. This is not a trivial task as it involves mapping potentially unreliable sensor readings from complex Second Life simulations to a domain-specific abstract logical model of observed properties and/or events. This paper investigates this problem in the context of agent interactions in a multi-agent system simulated in Second Life. We present a framework that facilitates the connection of any multi-agent platform with Second Life, and demonstrate it in conjunction with an extension of the Jason BDI interpreter.

Embedding agents in business applications using enterprise integration patterns

This paper addresses the issue of integrating agents with a variety of external resources and services, as found in enterprise computing environments. We propose an approach for interfacing agents and existing message routing and mediation engines based on the endpoint concept from the enterprise integration patterns of Hohpe and Woolf. A design for agent endpoints is presented, and an architecture for connecting the Jason agent platform to the Apache Camel enterprise integration framework using this type of endpoint is described. The approach is illustrated by means of a business process use case, and a number of Camel routes are presented. These demonstrate the benefits of interfacing agents to external services via a specialised message routing tool that supports enterprise integration patterns.

Improving situation awareness in intelligent virtual agents

Virtual worlds are inherently complex, dynamic and unpredictable in nature. The interface they provide to external agent systems consists of low-level events and primitive data. This introduces an information representation gap between virtual worlds and declarative BDI-based agent systems. As a result, BDI-based intelligent virtual agents (IVAs) are not capable of identifying the complex abstract situations unfolding in their surrounding environment. In this paper, we describe a two-step process that enables an IVA to identify the complex situations they encounter. First, complex event recognition mechanisms are applied on the low-level sensor data received by an IVA. Complex events identified in the first step are compared against a domain-specific situation model to identify active situations. The situation model helps the agent to be aware of the start and end of situations, and also to be aware of any active situation at any given time.

Integrating expectation monitoring into BDI agents

Although expectations play an important role in designing cognitive agents, monitoring for agent expectations is not explicitly being handled in most common agent programming environments. There are techniques for monitoring fulfilment and violation of agent expectations, however they are not linked with common agent programming environments so that agents can be easily programmed to respond to these circumstances. This paper investigates how to delegate this aspect of agent practical reasoning to an expectation monitoring tool integrated with a BDI agent platform. We exemplify this using the Jason BDI agent interpreter by extending it with built-in actions to initiate and terminate monitoring of expectations. This delegation enables agents to monitor for the fulfilment and violation of their expectations without relying on a centralised monitoring mechanism. This way, it is possible for agents to have plans that respond to the identified fulfilments and violations of their expectations.

Computer Aided Evaluation of Multi-Step Answers to Algebra Questions

This paper presents a system that automatically assesses multi-step answers to algebra questions. The system requires teacher involvement only during the question set-up stage. Two types of algebra questions are currently supported: questions with linear equations containing fractions, and questions with quadratic equations. The system evaluates each step of a students answer and awards full/partial marks according to a marking scheme. The system was evaluated for its performance using a set of student answer scripts from a government school in Sri Lanka and also by undergraduate students. The system accuracy was over 95.4%, and over 97.5%, respectively for the aforementioned data sets.

Domain-Specific Term Extraction for Concept Identification in Ontology Construction

An ontology is a formal and explicit specification of a shared conceptualization. Manual construction of domain ontology does not adequately satisfy requirements of new applications, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that humans cannot achieve alone. Researchers have discussed ontology learning as a solution to overcome issues related to the manual construction of ontology. Ontology learning is either an automatic or semi-automatic process to apply methods for building ontology from scratch, or enriching or adapting an existing ontology. This research focuses on improving the process of term extraction for identifying concepts in ontology learning. Available approaches for term extraction process are limited in various ways. These limitations include: (1) obtaining domain-specific terms from a domain expert as seed words without automatically discovering them from the corpus, and (2) unsuitable usage of corpora in discovering domain-specific terms for multiple domains. Our study uses linguistic analysis and statistical calculations to extract domain-specific simple and complex terms to overcome this first limitation. To eliminate the second limitation, we use multiple contrastive corpora that reduce the biasness in using a single contrastive corpus. Evaluations show that our system is better at extracting terms when compared with the previous research that used the same corpora.

VISIRI - Distributed Complex Event Processing System for Handling Large Number of Queries

Complex event processing (CEP) systems are used to process event data from multiple sources to infer events corresponding to more complicated situations. Traditional CEP systems with central processing engines have failed to cater to the requirement of processing large number of events generated from a large number of geographically distributed sources. Distributed CEP systems have been identified as the best alternative for this. However, designing an optimal distributed CEP system is a non-trivial task, and many factors have to be considered when designing the same. This paper presents the VISIRI distributed CEP system, which focuses on the problem of optimally processing a large number of different type of event streams using a large number of CEP queries in a distributed manner. The CEP query distribution algorithm in VISIRI is able to distribute a large number of queries among a set of CEP nodes in such a way that the event duplication in the network is minimized while not compromising the overall throughput of the system.

Automatic Identification of Errors in Multi-step Answers to Algebra Questions

This paper presents a system that automatically identifies errors made by students in answering algebra questions that require multiple steps. The types of algebra questions we consider include linear equations with fractions and quadratic equations. We have already developed a system that is capable of grading multi-step answers to the aforementioned two types of questions and awarding full/ partial credit according to a marking scheme. The error identification module works on top of this previous system. It was evaluated using data from two sources: government schools and a tuition class in Sri Lanka. The mistakes identified by the system were compared against feedback by two independent teachers. The results showed that the system identified the student mistakes with more than 85% accuracy for both types of questions.

Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words.

For aspect-level sentiment analysis, the important first step is to identify the aspects and their associated entities present in customer reviews. Aspects can be either explicit or implicit, where the identification of the latter is more difficult. For restaurant reviews, this difficulty is escalated due to the vast number of entities and aspects present in reviews. The problem of implicit aspect identification has been studied for customer reviews in different domains, including restaurant reviews. However, the existing work for implicit aspect identification in customer reviews has the limitation of choosing at most one implicit aspect for each sentence. Furthermore, they deal only with a limited set of aspects related to a particular domain, thus have not faced the problem of ambiguity that arises when an opinion word is used to describe different aspects. This paper presents a novel approach for implicit aspect detection, which overcomes these two limitations. Our approach yields an F1measure of 0.842 when applied for a set of restaurant reviews collected from Yelp.