12 research outputs found
Generating Context-Aware Contrastive Explanations in Rule-based Systems
Human explanations are often contrastive, meaning that they do not answer the
indeterminate "Why?" question, but instead "Why P, rather than Q?".
Automatically generating contrastive explanations is challenging because the
contrastive event (Q) represents the expectation of a user in contrast to what
happened. We present an approach that predicts a potential contrastive event in
situations where a user asks for an explanation in the context of rule-based
systems. Our approach analyzes a situation that needs to be explained and then
selects the most likely rule a user may have expected instead of what the user
has observed. This contrastive event is then used to create a contrastive
explanation that is presented to the user. We have implemented the approach as
a plugin for a home automation system and demonstrate its feasibility in four
test scenarios.Comment: 2024 Workshop on Explainability Engineering (ExEn '24
SmartEx: A Framework for Generating User-Centric Explanations in Smart Environments
Explainability is crucial for complex systems like pervasive smart
environments, as they collect and analyze data from various sensors, follow
multiple rules, and control different devices resulting in behavior that is not
trivial and, thus, should be explained to the users. The current approaches,
however, offer flat, static, and algorithm-focused explanations. User-centric
explanations, on the other hand, consider the recipient and context, providing
personalized and context-aware explanations. To address this gap, we propose an
approach to incorporate user-centric explanations into smart environments. We
introduce a conceptual model and a reference architecture for characterizing
and generating such explanations. Our work is the first technical solution for
generating context-aware and granular explanations in smart environments. Our
architecture implementation demonstrates the feasibility of our approach
through various scenarios.Comment: 22nd International Conference on Pervasive Computing and
Communications (PerCom 2024
Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020
From MDPI via Jisc Publications RouterHistory: accepted 2021-05-13, pub-electronic 2021-05-14Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; Grant(s): EP/R026092, EP/R026173, EP/R026084, 694277Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas
Explainable artificial intelligence for developing smart cities solutions
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of âexplainable deep learningâ as a subset of the âexplainable AIâ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating expertsâ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results