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Found 31 papers, 9 papers with code
Explicit Gradient Learning for Black-Box Optimization
We prove the convergence of EGL to a stationary point and its robustness in the optimization of integrable functions.
Intelligent Agents for Auction-based Federated Learning: A Survey
To enhance the efficiency in AFL decision support for stakeholders (i. e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged.
The Impact of Snippet Reliability on Misinformation in Online Health Search
These results strongly suggest that an information need-focused approach can significantly improve the reliability of extracted snippets in online health search.
Cognitive Effects in Large Language Models
Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day.
Contrastive Explanations of Centralized Multi-agent Optimization Solutions
We propose CMAoE, a domain-independent approach to obtain contrastive explanations by: (i) generating a new solution $S^\prime$ where property $P$ is enforced, while also minimizing the differences between $S$ and $S^\prime$; and (ii) highlighting the differences between the two solutions, with respect to the features of the objective function of the multi-agent system.
Explainable Multi-Agent Reinforcement Learning for Temporal Queries
As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments.
A Coupled Flow Approach to Imitation Learning
In reinforcement learning and imitation learning, an object of central importance is the state distribution induced by the policy.
Resource Sharing Through Multi-Round Matchings
For a general class of benefit functions satisfying certain properties (including diminishing returns), we show that this multi-round matching problem is efficiently solvable.
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise
Our focus is on resource allocation problems where agents may have restrictions that make them incompatible with some resources.
Efficient Customer Service Combining Human Operators and Virtual Agents
The prospect of combining human operators and virtual agents (bots) into an effective hybrid system that provides proper customer service to clients is promising yet challenging.
Not Just Skipping. Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health Search
We further discover that the contrast between the indirect marketing ads and the viewpoint presented in the organic search results plays an important role in users' decision-making.
Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction
In many social-choice mechanisms the resulting choice is not the most preferred one for some of the participants, thus the need for methods to justify the choice made in a way that improves the acceptance and satisfaction of said participants.
Robust Solutions for Multi-Defender Stackelberg Security Games
In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters.
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving.
Explaining Preference-driven Schedules: the EXPRES Framework
The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones.
Uncertainty with UAV Search of Multiple Goal-oriented Targets
We suggest a real-time algorithmic framework for the UAVs, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful detection of all of the targets.
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
In particular, the input of the Q-function is both the state and the action, and in multi-task problems (Meta-RL) the policy can take a state and a context.
Advising Agent for Service-Providing Live-Chat Operators
Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce.
Broadly Applicable Targeted Data Sample Omission Attacks
We show that, with a low attack budget, our attack's success rate is above 80%, and in some cases 100%, for white-box learning.
DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data
Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements.
Explicit Gradient Learning
Instead of fitting the function, EGL trains a NN to estimate the objective gradient directly.
AI for Explaining Decisions in Multi-Agent Environments
Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known.
Safe Policy Learning from Observations
The primary advantages of our approach, termed Rerouted Behavior Improvement (RBI), over other safe learning methods are its stability in the presence of value estimation errors and the elimination of a policy search process.
Providing Explanations for Recommendations in Reciprocal Environments
These platforms often include recommender systems that assist users in finding a suitable match.
Constrained Policy Improvement for Safe and Efficient Reinforcement Learning
To minimize the improvement penalty, the RBI idea is to attenuate rapid policy changes of low probability actions which were less frequently sampled.
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects
However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer's part.
Learning to Schedule Deadline- and Operator-Sensitive Tasks
The use of semi-autonomous and autonomous robotic assistants to aid in care of the elderly is expected to ease the burden on human caretakers, with small-stage testing already occurring in a variety of countries.
Human-Agent Decision-making: Combining Theory and Practice
In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people.
Psychologically based Virtual-Suspect for Interrogative Interview Training
In this paper, we present a Virtual-Suspect system which can be used to train inexperienced law enforcement personnel in interrogation strategies.
Using the Crowd to Generate Content for Scenario-Based Serious-Games
We found that the generated scenarios were rated as reliable and consistent by the crowd when compared to the scenarios that were originally captured.
