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Found 15 papers, 3 papers with code
Learning not to Regret
To scale to games that cannot fit in memory, we can use search with value functions.
Over-communicate no more: Situated RL agents learn concise communication protocols
While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other.
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization
Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently.
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals.
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study
Additionally, we compare two different agent architectures to assess how representational choices in agent design affect the human-agent interaction.
Player of Games
Games have a long history of serving as a benchmark for progress in artificial intelligence.
Emergent Communication at Scale
Emergent communication aims for a better understanding of human language evolution and building more efficient representations.
Solving Common-Payoff Games with Approximate Policy Iteration
While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
The E2E NLG Challenge: A Tale of Two Systems
This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system.
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
This paper describes the system deployed by the CLaC-EDLK team to the "SemEval 2016, Complex Word Identification task".
CLaC @ QATS: Quality Assessment for Text Simplification
This paper describes our approach to the 2016 QATS quality assessment shared task.
On the Contribution of Discourse Structure on Text Complexity Assessment
This paper investigates the influence of discourse features on text complexity assessment.
The CLaC Discourse Parser at CoNLL-2015
This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing.
Automatic Identification of AltLexes using Monolingual Parallel Corpora
The automatic identification of discourse relations is still a challenging task in natural language processing.
