State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.Comment: Under revie

Reactive Rules for Emergency Management

The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible

Learner Modelled Environments

Learner modelled environments (LMEs) are digital environments that are capable of automatically detecting learner’s behaviours in relation to a specific knowledge domain, to reason about those behaviours in order to asses learner’s performance, skills, socio-emotional and cognitive needs, and to act accordingly in a pedagogically appropriate manner. Digital environments that possess such capabilities are typically referred to as Intelligent Learning Environments, or more traditionally – as Intelligent Tutoring Systems (ITSs)

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System Roya Aminikia Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training materials, and corresponding assessments to guarantee an effective learning process. Although these systems are capable of distributing the learning content, they do not support dynamic learning processes and do not have the capability to communicate with human learners who are required to interact in a dynamic environment during the learning process. To create this process and support the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main objective of an ITS is to facilitate students’ movement towards their learning goals through virtual tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with access to a tutor who is available anytime during the learning session. The crucial issues we address in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories and foundations that we leverage to tackle these issues. We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge of students’ feature measurements during students’ guiding process. Therefore, the PA retains an updated status, called image, of each specific student at any moment. The agent uses this image for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to select the best individual direction. The significant capability of the agent is the ability to update its functionality by looking at a student’s image at run time. We also applied two supervised machine learning methods to improve the decision making protocol performance in order to maximize the effect of the collaborating mechanism between students and the ITS. Through these methods, we made the necessary modifications to the decision making structure to promote students’ performance by offering prompts during the learning sessions. The conducted experiments showed that the proposed system is able to efficiently classify students into learners with high versus low performance. Deployment of such a model enabled the PA to use different decision trees while interacting with students of different learning skills. The performance of the system has been shown by ROC curves and details regarding combination of different attributes used in the two machine learning algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy and performance of the decision maker components