Summary of Using Deep Q-learning to Dynamically Toggle Between Push/pull Actions in Computational Trust Mechanisms, by Zoi Lygizou and Dimitris Kalles
Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms
by Zoi Lygizou, Dimitris Kalles
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract presents a new decentralized computational trust model called CA, inspired by biological principles. The model focuses on the trustee’s perspective and addresses a long-standing issue in existing trust and reputation models: handling changing behaviors and agents’ entry/exit from the system. A comparison with FIRE, a well-known trust model, shows that CA outperforms it when the trustor population changes. This paper investigates how trustors can detect dynamic factors in their environment and decide which trust model to use to maximize utility. The problem is framed as a machine learning task in a partially observable environment, where the presence of dynamic factors is unknown to the trustor. Deep Q Learning (DQN) is used in a single-agent Reinforcement Learning setting to learn how to adapt to changing environments. Simulation experiments compare the performance of adaptable trustors with those using only one model and show that an adaptable agent can consistently perform well in dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way for computers to decide who to trust online. Right now, there are lots of problems with how we do trust online, like when someone changes their behavior or joins/leaves a group. A team came up with a new idea called CA that’s better at handling these kinds of changes. They compared it to another popular way called FIRE and found that CA does better when the people you’re trusting change. Now they want to figure out how the trustworthy computers can tell what’s going on in their world and decide which trust method to use. They used a special kind of learning called Reinforcement Learning to help these computers learn how to adapt to changing situations. They ran some computer simulations to see how well this adaptable approach works, and it looks like it can make good decisions even when things are changing quickly. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
