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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
chunk_id: string
embedding: list<item: double>
child 0, item: double
abstract: string
text: string
title: string
authors: string
id: string
submitted: timestamp[s]
primary_cat: string
categories: string
to
{'id': Value('string'), 'title': Value('string'), 'abstract': Value('string'), 'authors': Value('string'), 'categories': Value('string'), 'primary_cat': Value('string'), 'submitted': Value('timestamp[s]'), 'text': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
chunk_id: string
embedding: list<item: double>
child 0, item: double
abstract: string
text: string
title: string
authors: string
id: string
submitted: timestamp[s]
primary_cat: string
categories: string
to
{'id': Value('string'), 'title': Value('string'), 'abstract': Value('string'), 'authors': Value('string'), 'categories': Value('string'), 'primary_cat': Value('string'), 'submitted': Value('timestamp[s]'), 'text': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | title string | abstract string | authors string | categories string | primary_cat string | submitted timestamp[s] | text string |
|---|---|---|---|---|---|---|---|
2109.14155 | Customs Fraud Detection in the Presence of Concept Drift | Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current paper proposes ADAPT, an adaptive selection method that contro... | Tung-Duong Mai; Kien Hoang; Aitolkyn Baigutanova; Gaukhartas Alina; Sundong Kim | cs.AI cs.LG cs.NE | cs.AI | 2021-09-29T00:00:00 | Customs Fraud Detection in the Presence of Concept Drift. Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current pape... |
2105.01159 | Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks | Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electron... | Farnaz H. Foomani; D. M. Anisuzzaman; Jeffrey Niezgoda; Jonathan Niezgoda; William Guns; Sandeep Gopalakrishnan; Zeyun Yu | cs.AI cs.LG | cs.AI | 2021-05-03T00:00:00 | Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks. Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or a... |
2110.08012 | A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead | Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation syst... | Ali Hur; Naeem Janjua; Mohiuddin Ahmed | cs.AI cs.DB | cs.AI | 2021-12-31T00:00:00 | A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead. Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph ena... |
2111.03048 | Imagine Networks | In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples gener... | Seokjun Kim; Jaeeun Jang; Hyeoncheol Kim | cs.AI | cs.AI | 2021-12-30T00:00:00 | Imagine Networks. In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or ... |
2112.02498 | Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI | Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR framewo... | Jinchuan Tian; Jianwei Yu; Chao Weng; Shi-Xiong Zhang; Dan Su; Dong Yu; Yuexian Zou | cs.AI cs.CL | cs.AI | 2021-12-30T00:00:00 | Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI. Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria... |
2112.14770 | Proceedings of the 13th International Conference on Automated Deduction in Geometry | Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. Relevant topics include (but are not limited to): polynomial algebra, invariant and coordinate-free meth... | Predrag Janičić; Zoltán Kovács | cs.AI cs.LO cs.MS cs.SC | cs.AI | 2021-12-28T00:00:00 | Proceedings of the 13th International Conference on Automated Deduction in Geometry. Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. Relevant topics inc... |
2112.15221 | Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning | Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforceme... | Tong Mu; Georgios Theocharous; David Arbour; Emma Brunskill | cs.AI | cs.AI | 2021-12-30T00:00:00 | Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning. Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for inco... |
2112.15422 | Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021) | The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial. We contest the underlying assumption of Silver et al. that such reward can be scalar-valued. In this paper we explain why scala... | Peter Vamplew; Benjamin J. Smith; Johan Kallstrom; Gabriel Ramos; Roxana Radulescu; Diederik M. Roijers; Conor F. Hayes; Fredrik Heintz; Patrick Mannion; Pieter J. K. Libin; Richard Dazeley; Cameron Foale | cs.AI | cs.AI | 2021-11-25T00:00:00 | Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial. We contest the underlying assumption of Sil... |
2112.15475 | Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences | Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical applications and emerging neuromorphic and nanoscale hardware. HDC/VSA operate with hypervectors, i.e... | Dmitri A. Rachkovskij | cs.AI cs.LG cs.NE | cs.AI | 2021-12-31T00:00:00 | Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences. Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical applications and em... |
2112.15544 | OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies | OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity. If the components of an architecture also exploit OWL ontologies for kno... | Luca Buoncompagni; Syed Yusha Kareem; Fulvio Mastrogiovanni | cs.AI cs.LO cs.SE | cs.AI | 2021-12-31T00:00:00 | OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies. OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity.... |
2004.05002 | Self Punishment and Reward Backfill for Deep Q-Learning | Reinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, leading the agent to experience ambiguity in terms of whether those action... | Mohammad Reza Bonyadi; Rui Wang; Maryam Ziaei | cs.AI cs.LG | cs.AI | 2022-01-01T00:00:00 | Self Punishment and Reward Backfill for Deep Q-Learning. Reinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, leading the agent... |
2105.12328 | D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network | Counterfactual Regret Minimization (CFR)} is the popular method for finding approximate Nash equilibrium in two-player zero-sum games with imperfect information. CFR solves games by travsersing the full game tree iteratively, which limits its scalability in larger games. When applying CFR to solve large-scale games in ... | Huale Li; Xuan Wang; Zengyue Guo; Jiajia Zhang; Shuhan Qi | cs.AI | cs.AI | 2022-01-03T00:00:00 | D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network. Counterfactual Regret Minimization (CFR)} is the popular method for finding approximate Nash equilibrium in two-player zero-sum games with imperfect information. CFR solves games by travsersing the full game tree iteratively, which limits its scala... |
2112.09169 | On Optimizing Interventions in Shared Autonomy | Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performance, it may often also be beneficial that the agent concurrently accounts for preserving the user's experience or satisfaction of collaboratio... | Weihao Tan; David Koleczek; Siddhant Pradhan; Nicholas Perello; Vivek Chettiar; Vishal Rohra; Aaslesha Rajaram; Soundararajan Srinivasan; H M Sajjad Hossain; Yash Chandak | cs.AI cs.LG | cs.AI | 2022-01-01T00:00:00 | On Optimizing Interventions in Shared Autonomy. Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performance, it may often also be beneficial that the agent concurrently accounts for preserving the u... |
2201.00180 | IoT-based Route Recommendation for an Intelligent Waste Management System | The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; how... | Mohammadhossein Ghahramani; Mengchu Zhou; Anna Molter; Francesco Pilla | cs.AI | cs.AI | 2022-01-01T00:00:00 | IoT-based Route Recommendation for an Intelligent Waste Management System. The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with di... |
2201.00304 | Informed Multi-context Entity Alignment | Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreo... | Kexuan Xin; Zequn Sun; Wen Hua; Wei Hu; Xiaofang Zhou | cs.AI cs.CL | cs.AI | 2022-01-02T00:00:00 | Informed Multi-context Entity Alignment. Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in ca... |
2201.00548 | Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism | The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings. Since traditional methods cannot dynamically generate effective scheduling str... | Yunhui Zeng; Zijun Liao; Yuanzhi Dai; Rong Wang; Xiu Li; Bo Yuan | cs.AI cs.LG | cs.AI | 2022-01-03T00:00:00 | Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism. The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realisti... |
2201.00668 | Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure | Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an evaluator to guide the process, the idea is to automatically learn a policy able to return feasible and h... | Matteo Boffa; Zied Ben Houidi; Jonatan Krolikowski; Dario Rossi | cs.AI cs.LG cs.NI | cs.AI | 2022-01-03T00:00:00 | Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure. Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an... |
2201.00764 | Have I done enough planning or should I plan more? | People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, we show that people acquire this ability through lear... | Ruiqi He; Yash Raj Jain; Falk Lieder | cs.AI cs.LG | cs.AI | 2022-01-03T00:00:00 | Have I done enough planning or should I plan more?. People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, w... |
2012.15234 | Artificial Intelligence Development Races in Heterogeneous Settings | Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortch... | Theodor Cimpeanu; Francisco C. Santos; Luis Moniz Pereira; Tom Lenaerts; The Anh Han | cs.AI cs.GT | cs.AI | 2022-01-04T00:00:00 | Artificial Intelligence Development Races in Heterogeneous Settings. Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, saf... |
2102.08211 | The Yin-Yang dataset | The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios for both network models and hardware platforms, for which it provid... | Laura Kriener; Julian Göltz; Mihai A. Petrovici | cs.AI cs.NE q-bio.NC | cs.AI | 2022-01-04T00:00:00 | The Yin-Yang dataset. The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios for both network models and hardware platform... |
2106.00669 | Discovering Diverse Nearly Optimal Policies with Successor Features | Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose Diverse Successive Policies, a method for dis... | Tom Zahavy; Brendan O'Donoghue; Andre Barreto; Volodymyr Mnih; Sebastian Flennerhag; Satinder Singh | cs.AI cs.LG stat.ML | cs.AI | 2022-01-04T00:00:00 | Discovering Diverse Nearly Optimal Policies with Successor Features. Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and... |
2110.05028 | The CaLiGraph Ontology as a Challenge for OWL Reasoners | CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing more than 15 million typed entities and around 10 million relation assertions. Other than knowledge graphs such as DBpedia and YAGO, whose on... | Nicolas Heist; Heiko Paulheim | cs.AI | cs.AI | 2022-01-04T00:00:00 | The CaLiGraph Ontology as a Challenge for OWL Reasoners. CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing more than 15 million typed entities and around 10 million relation assertions. Other... |
2112.10892 | A Constraint Programming Approach to Weighted Isomorphic Mapping of Fragment-based Shape Signatures | Fragment-based shape signature techniques have proven to be powerful tools for computer-aided drug design. They allow scientists to search for target molecules with some similarity to a known active compound. They do not require reference to the full underlying chemical structure, which is essential to deal with chemic... | Thierry Petit; Randy J. Zauhar | cs.AI | cs.AI | 2022-01-04T00:00:00 | A Constraint Programming Approach to Weighted Isomorphic Mapping of Fragment-based Shape Signatures. Fragment-based shape signature techniques have proven to be powerful tools for computer-aided drug design. They allow scientists to search for target molecules with some similarity to a known active compound. They do no... |
2201.01027 | A integrating critic-waspas group decision making method under interval-valued q-rung orthogonal fuzzy enviroment | This paper provides a new tool for multi-attribute multi-objective group decision-making with unknown weights and attributes' weights. An interval-valued generalized orthogonal fuzzy group decision-making method is proposed based on the Yager operator and CRITIC-WASPAS method with unknown weights. The method integrates... | Benting Wan; Shufen Zhou | cs.AI | cs.AI | 2022-01-04T00:00:00 | A integrating critic-waspas group decision making method under interval-valued q-rung orthogonal fuzzy enviroment. This paper provides a new tool for multi-attribute multi-objective group decision-making with unknown weights and attributes' weights. An interval-valued generalized orthogonal fuzzy group decision-making ... |
2201.01249 | ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions | One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Although commonly used eXplainable AI methods provide some insight into opaque algorithms, such explanations are usually convoluted and not readily... | Adriano Lucieri; Muhammad Naseer Bajwa; Stephan Alexander Braun; Muhammad Imran Malik; Andreas Dengel; Sheraz Ahmed | cs.AI cs.LG eess.IV | cs.AI | 2022-01-04T00:00:00 | ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions. One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Although commonly used eXplainable AI methods provide some ... |
2103.04918 | A Survey of Embodied AI: From Simulators to Research Tasks | There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with their environments from an egocentric perception similar to ... | Jiafei Duan; Samson Yu; Hui Li Tan; Hongyuan Zhu; Cheston Tan | cs.AI cs.LG | cs.AI | 2022-01-05T00:00:00 | A Survey of Embodied AI: From Simulators to Research Tasks. There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with ... |
2112.05438 | DEBACER: a method for slicing moderated debates | Subjects change frequently in moderated debates with several participants, such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of a... | Thomas Palmeira Ferraz; Alexandre Alcoforado; Enzo Bustos; André Seidel Oliveira; Rodrigo Gerber; Naíde Müller; André Corrêa d'Almeida; Bruno Miguel Veloso; Anna Helena Reali Costa | cs.AI cs.CL cs.LG | cs.AI | 2021-12-10T00:00:00 | DEBACER: a method for slicing moderated debates. Subjects change frequently in moderated debates with several participants, such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defin... |
2201.01375 | Open Geometry Prover Community Project | Mathematical proof is undoubtedly the cornerstone of mathematics. The emergence, in the last years, of computing and reasoning tools, in particular automated geometry theorem provers, has enriched our experience with mathematics immensely. To avoid disparate efforts,the Open Geometry Prover Community Project aims at ... | Nuno Baeta; Pedro Quaresma | cs.AI cs.LO | cs.AI | 2022-01-03T00:00:00 | Open Geometry Prover Community Project. Mathematical proof is undoubtedly the cornerstone of mathematics. The emergence, in the last years, of computing and reasoning tools, in particular automated geometry theorem provers, has enriched our experience with mathematics immensely. To avoid disparate efforts,the Open Ge... |
2201.01466 | Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence | Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has ac... | Matti Pietikäinen; Olli Silven | cs.AI cs.CV cs.LG | cs.AI | 2022-01-05T00:00:00 | Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence. Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and acad... |
2201.01729 | The intersection probability: betting with probability intervals | Probability intervals are an attractive tool for reasoning under uncertainty. Unlike belief functions, though, they lack a natural probability transformation to be used for decision making in a utility theory framework. In this paper we propose the use of the intersection probability, a transform derived originally for... | Fabio Cuzzolin | cs.AI math.PR math.ST stat.TH | cs.AI | 2022-01-05T00:00:00 | The intersection probability: betting with probability intervals. Probability intervals are an attractive tool for reasoning under uncertainty. Unlike belief functions, though, they lack a natural probability transformation to be used for decision making in a utility theory framework. In this paper we propose the use o... |
2103.05277 | Efficient Vertex-Oriented Polytopic Projection for Web-scale Applications | We consider applications involving a large set of instances of projecting points to polytopes. We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes. To do these projections e... | Rohan Ramanath; S. Sathiya Keerthi; Yao Pan; Konstantin Salomatin; Kinjal Basu | cs.AI cs.LG stat.ML | cs.AI | 2022-01-06T00:00:00 | Efficient Vertex-Oriented Polytopic Projection for Web-scale Applications. We consider applications involving a large set of instances of projecting points to polytopes. We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of ... |
2106.09538 | Exploring deterministic frequency deviations with explainable AI | Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years. DFDs are partially explained by the rapid adjustment of power gen... | Johannes Kruse; Benjamin Schäfer; Dirk Witthaut | cs.AI cs.SY eess.SY physics.data-an | cs.AI | 2021-06-14T00:00:00 | Exploring deterministic frequency deviations with explainable AI. Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years.... |
2201.01816 | Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria | A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particularly critical in situations when other agents have hidden, possibly misaligned motivations and goals. Social deduction games offer an avenue t... | Kavya Kopparapu; Edgar A. Duéñez-Guzmán; Jayd Matyas; Alexander Sasha Vezhnevets; John P. Agapiou; Kevin R. McKee; Richard Everett; Janusz Marecki; Joel Z. Leibo; Thore Graepel | cs.AI cs.LG cs.MA | cs.AI | 2022-01-05T00:00:00 | Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria. A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particularly critical in situations when other agents have hidden, possibly mi... |
2109.08078 | Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks | Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based sign... | Nasim Baharisangari; Kazuma Hirota; Ruixuan Yan; Agung Julius; Zhe Xu | cs.AI cs.LG | cs.AI | 2022-01-06T00:00:00 | Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks. Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks t... |
1907.04276 | A Conformance Checking-based Approach for Drift Detection in Business Processes | Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes over time are called concept drift and its detection is a big challenge in process m... | Víctor Gallego-Fontenla; Juan C. Vidal; Manuel Lama | cs.AI cs.LG | cs.AI | 2019-07-09T00:00:00 | A Conformance Checking-based Approach for Drift Detection in Business Processes. Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes ove... |
2101.06883 | CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering | With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods usually ignore the relationship between the data. Fortunately, the g... | Guangyu Huo; Yong Zhang; Junbin Gao; Boyue Wang; Yongli Hu; Baocai Yin | cs.AI | cs.AI | 2021-01-18T00:00:00 | CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering. With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing de... |
2107.12977 | The social dilemma in artificial intelligence development and why we have to solve it | While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines. We argue that a possible underlying cause for this is that AI developers face a social dilemma in AI development ethics, preventing the wid... | Inga Strümke; Marija Slavkovik; Vince I. Madai | cs.AI cs.CY cs.LG | cs.AI | 2021-11-17T00:00:00 | The social dilemma in artificial intelligence development and why we have to solve it. While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines. We argue that a possible underlying cause for this i... |
2111.00004 | Granule Description based on Compound Concepts | Concise granule descriptions for definable granules and approaching descriptions for indefinable granules are challenging and important issues in granular computing. The concept with only common attributes has been intensively studied. To investigate the granules with some special needs, we propose a novel type of comp... | Jianqin Zhou; Sichun Yang; Xifeng Wang; Wanquan Liu | cs.AI | cs.AI | 2022-01-07T00:00:00 | Granule Description based on Compound Concepts. Concise granule descriptions for definable granules and approaching descriptions for indefinable granules are challenging and important issues in granular computing. The concept with only common attributes has been intensively studied. To investigate the granules with som... |
2201.00716 | Modeling Associative Reasoning Processes | The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In p... | Claudia Schon; Ulrich Furbach; Marco Ragni | cs.AI q-bio.NC | cs.AI | 2022-01-07T00:00:00 | Modeling Associative Reasoning Processes. The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by a... |
2201.02158 | Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting | Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with ti... | Omid Orang; Petrônio Cândido de Lima Silva; Frederico Gadelha Guimarães | cs.AI cs.LG cs.NE | cs.AI | 2022-01-07T00:00:00 | Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting. Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the conce... |
2001.01592 | Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment | The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intelligence, Automatic Identification System (AIS) plays an... | Enmei Tu; Guanghao Zhang; Shangbo Mao; Lily Rachmawati; Guang-Bin Huang | cs.AI cs.LG | cs.AI | 2022-01-09T00:00:00 | Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment. The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic r... |
2102.11762 | School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget | Pommerman is a hybrid cooperative/adversarial multi-agent environment, with challenging characteristics in terms of partial observability, limited or no communication, sparse and delayed rewards, and restrictive computational time limits. This makes it a challenging environment for reinforcement learning (RL) approache... | Omkar Shelke; Hardik Meisheri; Harshad Khadilkar | cs.AI cs.LG cs.MA | cs.AI | 2021-02-24T00:00:00 | School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget. Pommerman is a hybrid cooperative/adversarial multi-agent environment, with challenging characteristics in terms of partial observability, limited or no communication, sparse and delayed rewards, and restrictive computational ti... |
2201.02297 | Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey | Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixtu... | Omid Orang; Petrônio Cândido de Lima e Silva; Frederico Gadelha Guimarães | cs.AI cs.LG cs.NE | cs.AI | 2022-01-10T00:00:00 | Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey. Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classi... |
2201.02705 | Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships | Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can com... | Eunice Jun; Audrey Seo; Jeffrey Heer; René Just | cs.AI cs.HC cs.PL stat.CO stat.OT | cs.AI | 2022-01-07T00:00:00 | Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships. Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumption... |
2201.02950 | Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety | Several different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different and debated claims about the importance or usefulness of their work in the near term, and for future... | Issa Rice; David Manheim | cs.AI cs.CY | cs.AI | 2022-01-09T00:00:00 | Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety. Several different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different an... |
2201.03538 | Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability | In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol. Unlike previous works that assume a ... | João G. Ribeiro; Cassandro Martinho; Alberto Sardinha; Francisco S. Melo | cs.AI cs.LG cs.MA | cs.AI | 2022-01-10T00:00:00 | Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability. In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unk... |
2008.04548 | DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy | Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several rotation-based translational methods have been developed to model composite relations using the product of a series ... | Haonan Lu; Hailin Hu; Xiaodong Lin | cs.AI cs.CL cs.LG | cs.AI | 2022-01-11T00:00:00 | DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy. Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several ro... |
2103.03796 | Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control | Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient t... | Ruidong Yan; Rui Jiang; Bin Jia; Jin Huang; Diange Yang | cs.AI cs.LG cs.SY eess.SY | cs.AI | 2022-01-11T00:00:00 | Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control. Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. Howeve... |
2104.00409 | quantum Case-Based Reasoning (qCBR) | Case-Based Reasoning (CBR) is an artificial intelligence approach to problem-solving with a good record of success. This article proposes using Quantum Computing to improve some of the key processes of CBR, such that a Quantum Case-Based Reasoning (qCBR) paradigm can be defined. The focus is set on designing and implem... | Parfait Atchade-Adelomou; Daniel Casado-Fauli; Elisabet Golobardes-Ribe; Xavier Vilasis-Cardona | cs.AI cs.ET cs.LG | cs.AI | 2022-01-11T00:00:00 | quantum Case-Based Reasoning (qCBR). Case-Based Reasoning (CBR) is an artificial intelligence approach to problem-solving with a good record of success. This article proposes using Quantum Computing to improve some of the key processes of CBR, such that a Quantum Case-Based Reasoning (qCBR) paradigm can be defined. The... |
2105.14517 | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning | Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly depen... | Jiaqi Chen; Jianheng Tang; Jinghui Qin; Xiaodan Liang; Lingbo Liu; Eric P. Xing; Liang Lin | cs.AI | cs.AI | 2022-01-11T00:00:00 | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning. Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions,... |
2201.03647 | CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning | Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events. The human mind, while retrospecting a given situation, think about questions such as "What was the cause of the given situation?", "What would be the effect of my action?", or "Which action le... | Utkarshani Jaimini; Amit Sheth | cs.AI | cs.AI | 2022-01-06T00:00:00 | CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning. Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events. The human mind, while retrospecting a given situation, think about questions such as "What wa... |
2201.03649 | An Accelerator for Rule Induction in Fuzzy Rough Theory | Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable artificial intelligence. However, in this era of big data, rule induction on the whole datasets is computationally intensive. So far, to the ... | Suyun Zhao; Zhigang Dai; Xizhao Wang; Peng Ni; Hengheng Luo; Hong Chen; Cuiping Li | cs.AI cs.LG | cs.AI | 2022-01-07T00:00:00 | An Accelerator for Rule Induction in Fuzzy Rough Theory. Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable artificial intelligence. However, in this era of big data, rule induction on the who... |
2201.03709 | Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making | In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimately connected to time and timing. In service of generating and receiving signals, humans and other a... | Andrew Butcher; Michael Bradley Johanson; Elnaz Davoodi; Dylan J. A. Brenneis; Leslie Acker; Adam S. R. Parker; Adam White; Joseph Modayil; Patrick M. Pilarski | cs.AI cs.LG cs.MA | cs.AI | 2022-01-11T00:00:00 | Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making. In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimately conne... |
2201.03824 | Acquisition and Representation of User Preferences Guided by an Ontology | Our food preferences guide our food choices and in turn affect our personal health and our social life. In this paper, we adopt an approach using a domain ontology expressed in OWL2 to support the acquisition and representation of preferences in formalism CP-Net. Specifically, we present the construction of the domain ... | Rahma Dandan; Sylvie Despres; Karima Sedki | cs.AI | cs.AI | 2022-01-11T00:00:00 | Acquisition and Representation of User Preferences Guided by an Ontology. Our food preferences guide our food choices and in turn affect our personal health and our social life. In this paper, we adopt an approach using a domain ontology expressed in OWL2 to support the acquisition and representation of preferences in ... |
2010.15255 | Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans | In situations where humans and robots are moving in the same space whilst performing their own tasks, predictable paths taken by mobile robots can not only make the environment feel safer, but humans can also help with the navigation in the space by avoiding path conflicts or not blocking the way. So predictable paths ... | Sriram Gopalakrishnan; Subbarao Kambhampati | cs.AI | cs.AI | 2022-01-11T00:00:00 | Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans. In situations where humans and robots are moving in the same space whilst performing their own tasks, predictable paths taken by mobile robots can not only make the environment feel safer, but humans can also help with the navigation in the ... |
2201.04349 | Video Intelligence as a component of a Global Security system | This paper describes the evolution of our research from video analytics to a global security system with focus on the video surveillance component. Indeed video surveillance has evolved from a commodity security tool up to the most efficient way of tracking perpetrators when terrorism hits our modern urban centers. As ... | Dominique Verdejo; Eunika Mercier-Laurent | cs.AI | cs.AI | 2022-01-12T00:00:00 | Video Intelligence as a component of a Global Security system. This paper describes the evolution of our research from video analytics to a global security system with focus on the video surveillance component. Indeed video surveillance has evolved from a commodity security tool up to the most efficient way of tracking... |
2201.04477 | DPCL: a Language Template for Normative Specifications | Several solutions for specifying normative artefacts (norms, contracts, policies) in a computational processable way have been presented in the literature. Legal core ontologies have been proposed to systematize concepts and relationships relevant to normative reasoning. However, no solution amongst those has achieved ... | Giovanni Sileno; Thomas van Binsbergen; Matteo Pascucci; Tom van Engers | cs.AI cs.FL cs.MA cs.PL cs.SC | cs.AI | 2022-01-12T00:00:00 | DPCL: a Language Template for Normative Specifications. Several solutions for specifying normative artefacts (norms, contracts, policies) in a computational processable way have been presented in the literature. Legal core ontologies have been proposed to systematize concepts and relationships relevant to normative rea... |
2201.04596 | MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators | DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in Datalo... | Dingmin Wang; Pan Hu; Przemysław Andrzej Wałęga; Bernardo Cuenca Grau | cs.AI cs.DB | cs.AI | 2022-01-12T00:00:00 | MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators. DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal on... |
2201.04841 | Transforming UNL graphs in OWL representations | Extracting formal knowledge (ontologies) from natural language is a challenge that can benefit from a (semi-) formal linguistic representation of texts, at the semantic level. We propose to achieve such a representation by implementing the Universal Networking Language (UNL) specifications on top of RDF. Thus, the mean... | David Rouquet; Valérie Bellynck; Christian Boitet; Vincent Berment | cs.AI | cs.AI | 2022-01-13T00:00:00 | Transforming UNL graphs in OWL representations. Extracting formal knowledge (ontologies) from natural language is a challenge that can benefit from a (semi-) formal linguistic representation of texts, at the semantic level. We propose to achieve such a representation by implementing the Universal Networking Language (U... |
2201.05393 | Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP | In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a classic combinatorial optimization problem: the Capacitated Vehicle Routing Problem (CVRP). We formalize this problem in the RL framework and compare two of the most promising RL approaches with traditional solving techniques on a set of bench... | Leo Ardon | cs.AI cs.LG | cs.AI | 2022-01-14T00:00:00 | Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP. In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a classic combinatorial optimization problem: the Capacitated Vehicle Routing Problem (CVRP). We formalize this problem in the RL framework and compare two of the most... |
2201.05528 | Reinforcement Learning based Air Combat Maneuver Generation | The advent of artificial intelligence technology paved the way of many researches to be made within air combat sector. Academicians and many other researchers did a research on a prominent research direction called autonomous maneuver decision of UAV. Elaborative researches produced some outcomes, but decisions that in... | Muhammed Murat Ozbek; Emre Koyuncu | cs.AI | cs.AI | 2022-01-14T00:00:00 | Reinforcement Learning based Air Combat Maneuver Generation. The advent of artificial intelligence technology paved the way of many researches to be made within air combat sector. Academicians and many other researchers did a research on a prominent research direction called autonomous maneuver decision of UAV. Elabora... |
2201.05576 | AI and the Sense of Self | After several winters, AI is center-stage once again, with current advances enabling a vast array of AI applications. This renewed wave of AI has brought back to the fore several questions from the past, about philosophical foundations of intelligence and common sense -- predominantly motivated by ethical concerns of A... | Srinath Srinivasa; Jayati Deshmukh | cs.AI | cs.AI | 2022-01-07T00:00:00 | AI and the Sense of Self. After several winters, AI is center-stage once again, with current advances enabling a vast array of AI applications. This renewed wave of AI has brought back to the fore several questions from the past, about philosophical foundations of intelligence and common sense -- predominantly motivate... |
2101.00058 | Conflict-driven Inductive Logic Programming | The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns Answer Set Programs (ASP). Learning such expressive programs widens the applicability of ILP considerably; for exam... | Mark Law | cs.AI | cs.AI | 2022-01-14T00:00:00 | Conflict-driven Inductive Logic Programming. The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns Answer Set Programs (ASP). Learning such expressive programs widens th... |
2102.10717 | Abstraction and Analogy-Making in Artificial Intelligence | Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike... | Melanie Mitchell | cs.AI | cs.AI | 2021-05-14T00:00:00 | Abstraction and Analogy-Making in Artificial Intelligence. Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI sy... |
2104.02137 | ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities | Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. W... | Hongming Zhang; Xin Liu; Haojie Pan; Haowen Ke; Jiefu Ou; Tianqing Fang; Yangqiu Song | cs.AI cs.CL | cs.AI | 2022-01-16T00:00:00 | ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities. Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge.... |
2112.11805 | Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding | We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to learn about the data-driven decision making process of large neural models. The models are queried using a symbolic logic language. Interaction ... | Benedikt Wagner; Artur d'Avila Garcez | cs.AI cs.LG | cs.AI | 2022-01-17T00:00:00 | Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding. We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to learn about the data-driven decision making process of large neur... |
2201.05651 | CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures | Predicting contextualised engagement in videos is a long-standing problem that has been popularly attempted by exploiting the number of views or the associated likes using different computational methods. The recent decade has seen a boom in online learning resources, and during the pandemic, there has been an exponent... | Sujit Roy; Gnaneswara Rao Gorle; Vishal Gaur; Haider Raza; Shoaib Jameel | cs.AI cs.LG | cs.AI | 2022-01-14T00:00:00 | CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures. Predicting contextualised engagement in videos is a long-standing problem that has been popularly attempted by exploiting the number of views or the associated likes using different computational methods. The recent decade has seen ... |
2201.05658 | Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents | A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possib... | Ramon Pires; Fábio C. de Souza; Guilherme Rosa; Roberto A. Lotufo; Rodrigo Nogueira | cs.AI cs.CL | cs.AI | 2022-01-14T00:00:00 | Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents. A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes bein... |
2201.05710 | Specifying and Reasoning about CPS through the Lens of the NIST CPS Framework | This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer... | Thanh Hai Nguyen; Matthew Bundas; Tran Cao Son; Marcello Balduccini; Kathleen Campbell Garwood; Edward R. Griffor | cs.AI cs.LO | cs.AI | 2022-01-14T00:00:00 | Specifying and Reasoning about CPS through the Lens of the NIST CPS Framework. This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems relate... |
2201.05910 | An Automatic Ontology Generation Framework with An Organizational Perspective | Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus. Unfortunately, systems that aim to g... | Samaa Elnagar; Victoria Yoon; Manoj A. Thomas | cs.AI | cs.AI | 2022-01-15T00:00:00 | An Automatic Ontology Generation Framework with An Organizational Perspective. Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology... |
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