csAI

Decrement Operators in Belief Change. (arXiv:1905.08347v1 [cs.AI])

While research on iterated revision is predominant in the field of iterated belief change, the class of iterated contraction operators received more attention in recent years. In this article, we...

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Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs. (arXiv:1905.07465v1 [cs.AI])

Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making. We model patient-clinician interactions as...

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Knowledge-Based Sequential Decision-Making Under Uncertainty. (arXiv:1905.07030v1 [cs.AI])

Deep reinforcement learning (DRL) algorithms have achieved great success on sequential decision-making problems, yet is criticized for the lack of data-efficiency and explainability. Especially, explainability of subtasks is critical in...

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A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains. (arXiv:1905.07028v1 [cs.AI])

Finite-state controllers (FSCs), such as plans with loops, are powerful and compact representations of action selection widely used in robotics, video games and logistics. There has been steady progress on...

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Knowledge-based multi-level aggregation for decision aid in the machining industry. (arXiv:1905.06413v1 [cs.AI])

[ad_1] In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their...

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Improved Safe Real-time Heuristic Search. (arXiv:1905.06402v1 [cs.AI])

[ad_1] A fundamental concern in real-time planning is the presence of dead-ends in the state space, from which no goal is reachable. Recently, the SafeRTS algorithm was proposed for searching...

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Towards a Quantum-Like Cognitive Architecture for Decision-Making. (arXiv:1905.05176v1 [cs.AI])

[ad_1] We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than...

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MODL: A Modular Ontology Design Library. (arXiv:1904.05405v1 [cs.AI])

[ad_1] Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high...

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Optimizing Majority Voting Based Systems Under a Resource Constraint for Multiclass Problems. (arXiv:1904.04360v1 [cs.AI])

[ad_1] Ensemble-based approaches are very effective in various fields in raising the accuracy of its individual members, when some voting rule is applied for aggregating the individual decisions. In this...

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Domain Authoring Assistant for Intelligent Virtual Agents. (arXiv:1904.03266v1 [cs.AI])

[ad_1] Developing intelligent virtual characters has attracted a lot of attention in the recent years. The process of creating such characters often involves a team of creative authors who describe...

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Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice. (arXiv:1903.08218v1 [cs.AI])

[ad_1] Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations of solutions...

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Aesthetics of Neural Network Art. (arXiv:1903.05696v1 [cs.AI])

[ad_1] This paper proposes a way to understand neural network artworks as juxtapositions of natural image cues. It is hypothesized that images with unusual combinations of realistic visual cues are...

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Learning Self-Game-Play Agents for Combinatorial Optimization Problems. (arXiv:1903.03674v1 [cs.AI])

[ad_1] Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari games and...

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ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E. (arXiv:1903.03182v1 [cs.AI])

[ad_1] We describe an efficient implementation of clause guidance in saturation-based automated theorem provers extending the ENIGMA approach. Unlike in the first ENIGMA implementation where fast linear classifier is trained...

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Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? – A Case Study in Dynamic Courier Dispatching System. (arXiv:1903.02716v1 [cs.AI])

[ad_1] In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba. The CDP aims to assign a set of couriers to serve...

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Concurrent Meta Reinforcement Learning. (arXiv:1903.02710v1 [cs.AI])

[ad_1] State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner. A negative side-effect of this sequential execution paradigm...

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Discovering Options for Exploration by Minimizing Cover Time. (arXiv:1903.00606v1 [cs.AI])

[ad_1] One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is...

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Regret Minimisation in Multinomial Logit Bandits. (arXiv:1903.00543v1 [cs.AI])

[ad_1] We consider two regret minimisation problems over subsets of a finite ground set $[n]$, with subset-wise relative preference information feedback according to the Multinomial logit choice model. The first...

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Aggregating explainability methods for neural networks stabilizes explanations. (arXiv:1903.00519v1 [cs.AI])

[ad_1] Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation. In fact,...

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Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group. (arXiv:1903.00206v1 [cs.AI])

[ad_1] Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group...

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