csAI

That's Mine! Learning Ownership Relations and Norms for Robots. (arXiv:1812.02576v2 [cs.AI] UPDATED)

[ad_1] The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks...

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Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. (arXiv:1811.10433v9 [cs.AI] UPDATED)

[ad_1] In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In...

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The Tsetlin Machine – A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic. (arXiv:1804.01508v9 [cs.AI] UPDATED)

[ad_1] Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Unknown to many, there exists an arguably even simpler and more versatile learning mechanism, namely, the...

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Learning to Navigate in Cities Without a Map. (arXiv:1804.00168v3 [cs.AI] UPDATED)

[ad_1] Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a...

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What caused what? A quantitative account of actual causation using dynamical causal networks. (arXiv:1708.06716v2 [cs.AI] UPDATED)

[ad_1] Actual causation is concerned with the question "what caused what?" Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or...

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Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances". (arXiv:1901.03253v1 [cs.AI])

[ad_1] Humor is an essential human trait. Efforts to understand humor have called out links between humor and the foundations of cognition, as well as the importance of humor in...

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Self-Monitoring Navigation Agent via Auxiliary Progress Estimation. (arXiv:1901.03035v1 [cs.AI])

[ad_1] The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed,...

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PFML-based Semantic BCI Agent for Game of Go Learning and Prediction. (arXiv:1901.02999v1 [cs.AI])

[ad_1] This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally,...

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Making AI meaningful again. (arXiv:1901.02918v1 [cs.AI])

[ad_1] Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful...

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Robust and Adaptive Planning under Model Uncertainty. (arXiv:1901.02577v1 [cs.AI])

[ad_1] Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm,...

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High Fidelity Vector Space Models of Structured Data. (arXiv:1901.02565v1 [cs.AI])

[ad_1] Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would...

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Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss. (arXiv:1901.02035v1 [cs.AI])

[ad_1] We propose a generalized decision-theoretic system for a heterogeneous team of autonomous agents who are tasked with online identification of phenotypically expressed stress in crop fields.. This system employs...

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Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding. (arXiv:1901.00949v1 [cs.AI])

[ad_1] The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a...

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Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games. (arXiv:1901.00942v1 [cs.AI])

[ad_1] Decision-making problems can be modeled as combinatorial optimization problems with Constraint Programming formalisms such as Constrained Optimization Problems. However, few Constraint Programming formalisms can deal with both optimization and...

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Reachability and Differential based Heuristics for Solving Markov Decision Processes. (arXiv:1901.00921v1 [cs.AI])

[ad_1] The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states. In this paper, we present...

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Human-AI Learning Performance in Multi-Armed Bandits. (arXiv:1812.09376v1 [cs.AI])

[ad_1] People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at learning such tasks. Thus, there is...

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A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone. (arXiv:1812.09351v1 [cs.AI])

[ad_1] This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is...

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On the Relative Expressiveness of Bayesian and Neural Networks. (arXiv:1812.08957v1 [cs.AI])

[ad_1] A neural network computes a function. A central property of neural networks is that they are "universal approximators:" for a given continuous function, there exists a neural network that...

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Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach. (arXiv:1812.08947v1 [cs.AI])

[ad_1] The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for...

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Relevant Attributes in Formal Contexts. (arXiv:1812.08868v1 [cs.AI])

[ad_1] Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task. There are various approaches to deal with this, e.g., random sampling,...

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