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Found 15 papers, 2 papers with code
Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media
Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies.
Fundamental Properties of Causal Entropy and Information Gain
These measures, named causal entropy and causal information gain, aim to address limitations in existing information theoretical approaches for machine learning tasks where causality plays a crucial role.
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
In this work, we propose an RL algorithm that can automatically structure the reward function for sample efficiency, given a set of labels that signify subtasks.
Causal Entropy and Information Gain for Measuring Causal Control
Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable.
Providing personalized Explanations: a Conversational Approach
The increasing applications of AI systems require personalized explanations for their behaviors to various stakeholders since the stakeholders may have various knowledge and backgrounds.
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization
When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn.
A Survey of Multi-Agent Reinforcement Learning with Communication
Communication is an effective mechanism for coordinating the behavior of multiple agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
The Complexity of Data-Driven Norm Synthesis and Revision
In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective.
An Appraisal Transition System for Event-driven Emotions in Agent-based Player Experience Testing
In this paper, we propose an automated player experience testing approach by suggesting a formal model of event-based emotions.
Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs
The goal of entity matching in knowledge graphs is to identify entities that refer to the same real-world objects using some similarity metric.
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions
Reinforcement learning (RL) agents in human-computer interactions applications require repeated user interactions before they can perform well.
Dutch General Public Reaction on Governmental COVID-19 Measures and Announcements in Twitter Data
Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial.
Intention as Commitment toward Time
The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions.
Quantified Degrees of Group Responsibility (Extended Abstract)
This paper builds on an existing notion of group responsibility and proposes two ways to define the degree of group responsibility: structural and functional degrees of responsibility.
Expressibility of norms in temporal logic
In this short note we address the issue of expressing norms (such as obligations and prohibitions) in temporal logic.
