csAI

What deep learning can tell us about higher cognitive functions like mindreading?. (arXiv:1803.10470v2 [cs.AI] CROSS LISTED)

Can deep learning (DL) guide our understanding of computations happening in biological brain? We will first briefly consider how DL has contributed to the research on visual object recognition. In...

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On Open-Universe Causal Reasoning. (arXiv:1907.02170v1 [cs.AI])

We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces. This enables a semantics for conditionals founded on a calculus of intervention, and...

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Recommendations on Designing Practical Interval Type-2 Fuzzy Systems. (arXiv:1907.01697v1 [cs.AI])

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system...

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On Conflicting and Conflicting Values. (arXiv:1907.01682v1 [cs.AI])

Values are things that are important to us. Actions activate values - they either go against our values or they promote our values. Values themselves can either be conforming or...

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Evolving the Hearthstone Meta. (arXiv:1907.01623v1 [cs.AI])

Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system....

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E-Sports Talent Scouting Based on Multimodal Twitch Stream Data. (arXiv:1907.01615v1 [cs.AI])

We propose and investigate feasibility of a novel task that consists in finding e-sports talent using multimodal Twitch chat and video stream data. In that, we focus on predicting the...

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Visual analytics for team-based invasion sports with significant events and Markov reward process. (arXiv:1907.01221v1 [cs.AI])

In team-based invasion sports such as soccer and basketball, analytics is important for teams to understand their performance and for audiences to understand matches better. The present work focuses on...

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Investigating The Piece-Wise Linearity And Benchmark Related To Koczy-Hirota Fuzzy Linear Interpolation. (arXiv:1907.01047v1 [cs.AI])

Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to address sparse fuzzy rule bases and reduce complexity. The first FRI method was the Koczy and Hirota (KH) proposed "Linear...

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An Overview of the Ludii General Game System. (arXiv:1907.00240v1 [cs.AI])

The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000 traditional strategy games using modern techniques. One of the key aspects of this project is the development of...

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Generalization of Dempster-Shafer theory: A complex belief function. (arXiv:1906.11409v1 [cs.AI])

Dempster-Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. However, the existing evidence theory is...

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Generalization to Novel Objects using Prior Relational Knowledge. (arXiv:1906.11315v1 [cs.AI])

To solve tasks in new environments involving objects unseen during training, agents must reason over prior information about those objects and their relations. We introduce the Prior Knowledge Graph network,...

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Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps. (arXiv:1906.11247v1 [cs.AI])

Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event...

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Soft computing methods for multiobjective location of garbage accumulation points in smart cities. (arXiv:1906.10689v1 [cs.AI])

This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities,...

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Training an Interactive Helper. (arXiv:1906.10165v1 [cs.AI])

Developing agents that can quickly adapt their behavior to new tasks remains a challenge. Meta-learning has been applied to this problem, but previous methods require either specifying a reward function...

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Assembly line balancing with task division. (arXiv:1906.10120v1 [cs.AI])

In a commonly-used version of the Simple Assembly Line Balancing Problem (SALBP-1) tasks are assigned to stations along an assembly line with a fixed cycle time in order to minimize...

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Multi-view Knowledge Graph Embedding for Entity Alignment. (arXiv:1906.02390v1 [cs.AI])

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such...

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Risks from Learned Optimization in Advanced Machine Learning Systems. (arXiv:1906.01820v1 [cs.AI])

We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer - a situation we refer to as mesa-optimization,...

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The Stanford Acuity Test: A Probabilistic Approach for Precise Visual Acuity Testing. (arXiv:1906.01811v1 [cs.AI])

Chart-based visual acuity measurements are used by billions of people to diagnose and guide treatment of vision impairment. However, the ubiquitous eye exam has no mechanism for reasoning about uncertainty...

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Balanced Ranking with Diversity Constraints. (arXiv:1906.01747v1 [cs.AI])

Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the overall representativeness of...

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Decision-Making in Reinforcement Learning. (arXiv:1906.00131v1 [cs.AI])

In this research work, probabilistic decision-making approaches are studied, e.g. Bayesian and Boltzmann strategies, along with various deterministic exploration strategies, e.g. greedy, epsilon-Greedy and random approaches. In this research work,...

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