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This paper explores the use of LH Interval Calculus, a novel qualitative spatial reasoning formalism, to create a human-readable representation of environments observed by UAVs. The system simplifies data from multiple UAVs collaborating on ...

Event models, in the form of scripts, frames, or precondition/effect axioms, allow for reasoning about the causal and motivational connections between events in a story, and thus are central to AI understanding and generating narratives. However, ...

Detecting and adapting to novel situations is a major challenge for AI systems that operate in open-world environments. One reason for this challenge is due to the diverse range of forms that novelties can take. To accurately evaluate an AI system’...

In this paper, we present Lateral AI that offers a diverse and multi-dimensional world experience. It makes use of semi-automated prompt engineering on top of GPT3.5. The coupling with named entity recognition and text summarization enables ...

We consider belief change in a context where information comes from reports, and the reporting agents may not be honest. In order to capture this process, we introduce an extended class of epistemic states that includes a history of past reports ...

This research investigates the role of prompt engineering in enhancing the performance and generalisation of large-scale language models (LLMs) across a wide range of Natural Language Processing (NLP) tasks. The study introduces a comprehensive ...

Recent developments in computational machine ethics have adopted the assumption of a fully observable environment. However, such an assumption is not realistic for the ethical decision-making process. Epistemic reasoning is one approach to deal ...

Floods are amongst the most severe natural disasters. Accurate flood risk maps are vital for emergency response operations and long-term flood defence planning. Currently the validation of such maps is often neglected and suffers from a lack of ...

A growing number of organizations are adopting a strategy of breaking down large data analysis problems into specific sub-problems, tailoring models for each. However, handling a large number of individual models can pose challenges in ...

Enterprise knowledge graphs are gaining increasing popularity in industrial applications, with a pressing demand for natural language interfaces to support non-technical end-users. For natural language queries to relational databases, the neural ...

As an active research topic in computer vision, scene text recognition (STR) aims to recognize character sequences in natural scenes. Currently, mainstream STR approaches consist of two main modules: a visual model for feature extraction and a ...

Adapting to data distribution shifts after training remains a significant challenge within the realm of Artificial Intelligence. This paper presents a refined approach, superior to Automated Hyper Parameter Tuning methods, that effectively detects ...

Interpretable federated learning is an emerging challenge to identify explainable characteristics of each client-specific personalized model in a federated learning system. This paper proposes a novel federated concept bottleneck (FedCBM) method ...

Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data ...

In recent years, the interpretive this looks like that structure has gained significant attention. It refers to the human tendency to break down images into key parts and make classification decisions by comparing them to pre-existing concepts in ...

Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acceptance and deployment of AI-assisted ...

EXplainable machine learning (XML) has recently emerged as a promising approach to address the inherent opacity of machine learning (ML) systems by providing insights into their reasoning processes. This paper explores the relationships among user ...

As the number of people with psychological disorders continues to increase in recent years, it is particularly important to identify patients at an early stage. As one of the widely recognized methods of drawing psychoanalysis methods, the House-...

We are motivated by the problem of identifying potentially nonlinear regression relationships between high-dimensional outputs and high-dimensional inputs of heterogeneous data. This requires regression, clustering, and model selection, ...

We consider a task allocation problem in which agents and tasks have locations, and the goal is to allocate tasks among agents so as to minimise the distance travelled. We analyse two important algorithms under a generalised setting that puts ...

Automated Negotiation (AN) is a research field with roots extending back to the mid-twentieth century. There are two dominant AN research directions in recent years: (1) designing new heuristic strategies for the simplest bargaining protocol ...

Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to specify complete models, and ...

Expanding non-player character (NPC) navigation behavior in video games has the potential to induce novel player experiences. Current industry standards represent traversable world geometry by utilizing a Navigation Mesh (NavMesh); however NavMesh ...

Reinforcement learning algorithms require an extensive number of samples to perform a specific task. To achieve the same performance on a new task, the agent must learn from scratch. Transfer reinforcement learning is an emerging solution that ...

Cooperation in multi-agent reinforcement learning (MARL) facilitates the acquisition of complex problem-solving skills and promotes more efficient and effective decision-making among agents. Numerous strategies for cooperative learning in MARL ...