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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03080">arXiv:2502.03080</a> <span> [<a href="https://arxiv.org/pdf/2502.03080">pdf</a>, <a href="https://arxiv.org/format/2502.03080">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.03080v1-abstract-short" style="display: inline;"> While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, understanding and validating their knowledge utilization remains challenging. Chain-of-thought (CoT) prompting partially addresses this by revealing intermediate reasoning steps, but the knowledge flow and application remain implicit. We introduce IAO (Input-Action-Output) prompting, a structured template-based metho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03080v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03080v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03080v1-abstract-full" style="display: none;"> While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, understanding and validating their knowledge utilization remains challenging. Chain-of-thought (CoT) prompting partially addresses this by revealing intermediate reasoning steps, but the knowledge flow and application remain implicit. We introduce IAO (Input-Action-Output) prompting, a structured template-based method that explicitly models how LLMs access and apply their knowledge during complex reasoning tasks. IAO decomposes problems into sequential steps, each clearly identifying the input knowledge being used, the action being performed, and the resulting output. This structured decomposition enables us to trace knowledge flow, verify factual consistency, and identify potential knowledge gaps or misapplications. Through experiments across diverse reasoning tasks, we demonstrate that IAO not only improves zero-shot performance but also provides transparency in how LLMs leverage their stored knowledge. Human evaluation confirms that this structured approach enhances our ability to verify knowledge utilization and detect potential hallucinations or reasoning errors. Our findings provide insights into both knowledge representation within LLMs and methods for more reliable knowledge application. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03080v1-abstract-full').style.display = 'none'; document.getElementById('2502.03080v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted as Oral at KnowFM @ AAAI 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.03888">arXiv:2501.03888</a> <span> [<a href="https://arxiv.org/pdf/2501.03888">pdf</a>, <a href="https://arxiv.org/format/2501.03888">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Baugh%2C+K+G">Kexin Gu Baugh</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Russo%2C+A">Alessandra Russo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.03888v2-abstract-short" style="display: inline;"> Although deep reinforcement learning has been shown to be effective, the model's black-box nature presents barriers to direct policy interpretation. To address this problem, we propose a neuro-symbolic approach called neural DNF-MT for end-to-end policy learning. The differentiable nature of the neural DNF-MT model enables the use of deep actor-critic algorithms for training. At the same time, its… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03888v2-abstract-full').style.display = 'inline'; document.getElementById('2501.03888v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.03888v2-abstract-full" style="display: none;"> Although deep reinforcement learning has been shown to be effective, the model's black-box nature presents barriers to direct policy interpretation. To address this problem, we propose a neuro-symbolic approach called neural DNF-MT for end-to-end policy learning. The differentiable nature of the neural DNF-MT model enables the use of deep actor-critic algorithms for training. At the same time, its architecture is designed so that trained models can be directly translated into interpretable policies expressed as standard (bivalent or probabilistic) logic programs. Moreover, additional layers can be included to extract abstract features from complex observations, acting as a form of predicate invention. The logic representations are highly interpretable, and we show how the bivalent representations of deterministic policies can be edited and incorporated back into a neural model, facilitating manual intervention and adaptation of learned policies. We evaluate our approach on a range of tasks requiring learning deterministic or stochastic behaviours from various forms of observations. Our empirical results show that our neural DNF-MT model performs at the level of competing black-box methods whilst providing interpretable policies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03888v2-abstract-full').style.display = 'none'; document.getElementById('2501.03888v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AAMAS 2025 (with Appendix)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13919">arXiv:2409.13919</a> <span> [<a href="https://arxiv.org/pdf/2409.13919">pdf</a>, <a href="https://arxiv.org/format/2409.13919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Measuring Error Alignment for Decision-Making Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+B">Binxia Xu</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Onah%2C+D">Daniel Onah</a>, <a href="/search/cs?searchtype=author&query=Vlachidis%2C+A">Andreas Vlachidis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.13919v2-abstract-short" style="display: inline;"> Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the informa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13919v2-abstract-full').style.display = 'inline'; document.getElementById('2409.13919v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13919v2-abstract-full" style="display: none;"> Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the information processing similarities between AI and humans, may be able to achieve these same ends. While Representational alignment (RA) approaches measure similarity between the internal states of two systems, the associated data can be expensive and difficult to collect for human systems. In contrast, Behavioural alignment (BA) comparisons are cheaper and easier, but questions remain as to their sensitivity and reliability. We propose two new behavioural alignment metrics misclassification agreement which measures the similarity between the errors of two systems on the same instances, and class-level error similarity which measures the similarity between the error distributions of two systems. We show that our metrics correlate well with RA metrics, and provide complementary information to another BA metric, within a range of domains, and set the scene for a new approach to value alignment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13919v2-abstract-full').style.display = 'none'; document.getElementById('2409.13919v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.12088">arXiv:2401.12088</a> <span> [<a href="https://arxiv.org/pdf/2401.12088">pdf</a>, <a href="https://arxiv.org/format/2401.12088">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Learning of Graph from Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.12088v1-abstract-short" style="display: inline;"> Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and generate a graph to represent the sequence of actions in the recipe. In contrast with other approaches, we use an unsupervised approach. We iteratively… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12088v1-abstract-full').style.display = 'inline'; document.getElementById('2401.12088v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.12088v1-abstract-full" style="display: none;"> Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and generate a graph to represent the sequence of actions in the recipe. In contrast with other approaches, we use an unsupervised approach. We iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$ encoding the texts (text-to-graph) one sequence at a time while providing the supervision by decoding the graph into text (graph-to-text) and comparing the generated text to the input. We evaluate the approach by comparing the identified entities with annotated datasets, comparing the difference between the input and output texts, and comparing our generated graphs with those generated by state of the art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12088v1-abstract-full').style.display = 'none'; document.getElementById('2401.12088v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.06930">arXiv:2401.06930</a> <span> [<a href="https://arxiv.org/pdf/2401.06930">pdf</a>, <a href="https://arxiv.org/format/2401.06930">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.06930v2-abstract-short" style="display: inline;"> Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and output… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06930v2-abstract-full').style.display = 'inline'; document.getElementById('2401.06930v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.06930v2-abstract-full" style="display: none;"> Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe. We present a new corpus of cooking recipes enriched with descriptions of intermediate steps that describe the input and output for each step. PizzaCommonsense serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit input-output descriptions to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-4 achieves only 26\% human-evaluated preference for generations, leaving room for future improvements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06930v2-abstract-full').style.display = 'none'; document.getElementById('2401.06930v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Findings of EMNLP 2024. The data is available at: https://github.com/adiallo07/PizzaCommonsense</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.09042">arXiv:2306.09042</a> <span> [<a href="https://arxiv.org/pdf/2306.09042">pdf</a>, <a href="https://arxiv.org/ps/2306.09042">ps</a>, <a href="https://arxiv.org/format/2306.09042">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Graphical Formalism for Commonsense Reasoning with Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.09042v1-abstract-short" style="display: inline;"> Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework. We address this need by proposing a graphical formalization that captures the comestibles (ingredients, intermediate food items, and final products), and the actions on comestibles in the form of a labelled bipartite graph. We then propose f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09042v1-abstract-full').style.display = 'inline'; document.getElementById('2306.09042v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.09042v1-abstract-full" style="display: none;"> Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework. We address this need by proposing a graphical formalization that captures the comestibles (ingredients, intermediate food items, and final products), and the actions on comestibles in the form of a labelled bipartite graph. We then propose formal definitions for comparing recipes, for composing recipes from subrecipes, and for deconstructing recipes into subrecipes. We also introduce and compare two formal definitions for substitution into recipes which are required when there are missing ingredients, or some actions are not possible, or because there is a need to change the final product somehow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09042v1-abstract-full').style.display = 'none'; document.getElementById('2306.09042v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.10129">arXiv:2206.10129</a> <span> [<a href="https://arxiv.org/pdf/2206.10129">pdf</a>, <a href="https://arxiv.org/format/2206.10129">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Automatic Concept Extraction for Concept Bottleneck-based Video Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeyakumar%2C+J+V">Jeya Vikranth Jeyakumar</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+L">Luis Garcia</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+Y">Yu-Hsi Cheng</a>, <a href="/search/cs?searchtype=author&query=Echavarria%2C+D+R">Diego Ramirez Echavarria</a>, <a href="/search/cs?searchtype=author&query=Noor%2C+J">Joseph Noor</a>, <a href="/search/cs?searchtype=author&query=Russo%2C+A">Alessandra Russo</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L">Lance Kaplan</a>, <a href="/search/cs?searchtype=author&query=Blasch%2C+E">Erik Blasch</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+M">Mani Srivastava</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.10129v1-abstract-short" style="display: inline;"> Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-level visual concepts from images, e.g., identifying the wing color and beak length for bird-species classification. However, these concept bottleneck models rely on a necessary… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.10129v1-abstract-full').style.display = 'inline'; document.getElementById('2206.10129v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.10129v1-abstract-full" style="display: none;"> Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-level visual concepts from images, e.g., identifying the wing color and beak length for bird-species classification. However, these concept bottleneck models rely on a necessary and sufficient set of predefined concepts-which is intractable for complex tasks such as video classification. For complex tasks, the labels and the relationship between visual elements span many frames, e.g., identifying a bird flying or catching prey-necessitating concepts with various levels of abstraction. To this end, we present CoDEx, an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification. CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts. To demonstrate our method's viability, we construct two new public datasets that combine existing complex video classification datasets with short, crowd-sourced natural language explanations for their labels. Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.10129v1-abstract-full').style.display = 'none'; document.getElementById('2206.10129v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, Appendix: 2 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.08425">arXiv:2109.08425</a> <span> [<a href="https://arxiv.org/pdf/2109.08425">pdf</a>, <a href="https://arxiv.org/ps/2109.08425">ps</a>, <a href="https://arxiv.org/format/2109.08425">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Repurposing of Resources: from Everyday Problem Solving through to Crisis Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.08425v1-abstract-short" style="display: inline;"> The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.08425v1-abstract-full').style.display = 'inline'; document.getElementById('2109.08425v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.08425v1-abstract-full" style="display: none;"> The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.08425v1-abstract-full').style.display = 'none'; document.getElementById('2109.08425v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.4; I.2.6; I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.07137">arXiv:2011.07137</a> <span> [<a href="https://arxiv.org/pdf/2011.07137">pdf</a>, <a href="https://arxiv.org/format/2011.07137">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Str%C3%B6mfelt%2C+H">Harald Str枚mfelt</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Garcez%2C+A+d">Artur d'Avila Garcez</a>, <a href="/search/cs?searchtype=author&query=Russo%2C+A">Alessandra Russo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.07137v1-abstract-short" style="display: inline;"> We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties. We compare the relative benefits of relation-decoder complexity and latent space structure on both inductive and transductive transfer learning. Our results depict a complex picture where enforcing structure on semi-supervised… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.07137v1-abstract-full').style.display = 'inline'; document.getElementById('2011.07137v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.07137v1-abstract-full" style="display: none;"> We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties. We compare the relative benefits of relation-decoder complexity and latent space structure on both inductive and transductive transfer learning. Our results depict a complex picture where enforcing structure on semi-supervised representations can greatly improve zero-shot transductive transfer, but may be less favourable or even impact negatively the capacity for inductive transfer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.07137v1-abstract-full').style.display = 'none'; document.getElementById('2011.07137v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.01745">arXiv:2010.01745</a> <span> [<a href="https://arxiv.org/pdf/2010.01745">pdf</a>, <a href="https://arxiv.org/format/2010.01745">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On the Effects of Knowledge-Augmented Data in Word Embeddings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ramirez-Echavarria%2C+D">Diego Ramirez-Echavarria</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a>, <a href="/search/cs?searchtype=author&query=Vlachidis%2C+A">Andreas Vlachidis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.01745v1-abstract-short" style="display: inline;"> This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data. These representations are trained with word cooccurrence statistics and do not commonly exploit syntactic and semantic information from linguistic knowledge bases, which potentially limits their transferability to domains with differing language distributions or usages. W… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.01745v1-abstract-full').style.display = 'inline'; document.getElementById('2010.01745v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.01745v1-abstract-full" style="display: none;"> This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data. These representations are trained with word cooccurrence statistics and do not commonly exploit syntactic and semantic information from linguistic knowledge bases, which potentially limits their transferability to domains with differing language distributions or usages. We propose a novel approach for linguistic knowledge injection through data augmentation to learn word embeddings that enforce semantic relationships from the data, and systematically evaluate the impact it has on the resulting representations. We show our knowledge augmentation approach improves the intrinsic characteristics of the learned embeddings while not significantly altering their results on a downstream text classification task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.01745v1-abstract-full').style.display = 'none'; document.getElementById('2010.01745v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 5 figures, submitted to ACL 2020</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.03064">arXiv:1903.03064</a> <span> [<a href="https://arxiv.org/pdf/1903.03064">pdf</a>, <a href="https://arxiv.org/format/1903.03064">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abramova%2C+E">Ekaterina Abramova</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Kuhn%2C+D">Daniel Kuhn</a>, <a href="/search/cs?searchtype=author&query=Faisal%2C+A">Aldo Faisal</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.03064v1-abstract-short" style="display: inline;"> Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinforcement Learning Optimal Control, which operates on two levels of abstraction and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03064v1-abstract-full').style.display = 'inline'; document.getElementById('1903.03064v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.03064v1-abstract-full" style="display: none;"> Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinforcement Learning Optimal Control, which operates on two levels of abstraction and utilises a reduced number of controllers to solve nonlinear systems with unknown dynamics in continuous state and action spaces. Our approach is inspired by research at two levels of abstraction: first, at the level of limb coordination human behaviour is explained by linear optimal feedback control theory. Second, in cognitive tasks involving learning symbolic level action selection, humans learn such problems using model-free and model-based reinforcement learning algorithms. We propose that combining these two levels of abstraction leads to a fast global solution of nonlinear control problems using reduced number of controllers. Our framework learns the local task dynamics from naive experience and forms locally optimal infinite horizon Linear Quadratic Regulators which produce continuous low-level control. A top-level reinforcement learner uses the controllers as actions and learns how to best combine them in state space while maximising a long-term reward. A single optimal control objective function drives high-level symbolic learning by providing training signals on desirability of each selected controller. We show that a small number of locally optimal linear controllers are able to solve global nonlinear control problems with unknown dynamics when combined with a reinforcement learner in this hierarchical framework. Our algorithm competes in terms of computational cost and solution quality with sophisticated control algorithms and we illustrate this with solutions to benchmark problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03064v1-abstract-full').style.display = 'none'; document.getElementById('1903.03064v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.06815">arXiv:1703.06815</a> <span> [<a href="https://arxiv.org/pdf/1703.06815">pdf</a>, <a href="https://arxiv.org/ps/1703.06815">ps</a>, <a href="https://arxiv.org/format/1703.06815">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Foundations for a Probabilistic Event Calculus </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=D%27Asaro%2C+F+A">Fabio Aurelio D'Asaro</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.06815v2-abstract-short" style="display: inline;"> We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to that of the EC variant Modular-E. Its semantics is given in terms of possible worlds which constitute possible evolutions of the domain, and builds on that of EFEC, an epistemic extension of EC. We also describe an ASP im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.06815v2-abstract-full').style.display = 'inline'; document.getElementById('1703.06815v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.06815v2-abstract-full" style="display: none;"> We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to that of the EC variant Modular-E. Its semantics is given in terms of possible worlds which constitute possible evolutions of the domain, and builds on that of EFEC, an epistemic extension of EC. We also describe an ASP implementation of PEC and show the sense in which this is sound and complete. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.06815v2-abstract-full').style.display = 'none'; document.getElementById('1703.06815v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Technical report</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 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