Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Pynadath, David"'
Autor:
Huang, Shuo, Jones, Fred, Gurney, Nikolos, Pynadath, David, Srivastava, Kunal, Trent, Stoney, Wu, Peggy, Zhu, Quanyan
Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend against APTs. Cognitive vulnerabilities can significantly influence att
Externí odkaz:
http://arxiv.org/abs/2408.01310
We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of
Externí odkaz:
http://arxiv.org/abs/2402.13273
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science,
Externí odkaz:
http://arxiv.org/abs/2402.13272
Publikováno v:
AAMAS (2023) 708-716
We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer th
Externí odkaz:
http://arxiv.org/abs/2302.10238
Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact thei
Externí odkaz:
http://arxiv.org/abs/2302.01854
An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information -- ask them about an agent's benevolence, and they should consider whether or not it was kind. Behavioral science, how
Externí odkaz:
http://arxiv.org/abs/2301.09011
Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans
Externí odkaz:
http://arxiv.org/abs/2301.05969
Typical approaches to plan recognition start from a representation of an agent's possible plans, and reason evidentially from observations of the agent's actions to assess the plausibility of the various candidates. A more expansive view of the task
Externí odkaz:
http://arxiv.org/abs/1302.4980
Techniques for plan recognition under uncertainty require a stochastic model of the plan-generation process. We introduce Probabilistic State-Dependent Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG language model extends
Externí odkaz:
http://arxiv.org/abs/1301.3888
Akademický článek
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