Using a Priori Information for Fast Learning Against Non-stationary Opponents

Hernández-Leal, Pablo; Cote, Enrique Muñoz de; Sucar, L. Enrique

doi:10.1007/978-3-319-12027-0_43

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing MAS literature outside of the scope of trust and reputation has looked at identifying the best behaviour policy to use from a set of policies, by identifying other agents’ behaviours and responding to that. Part of this task involves identifying when other agents have changed their own policy (Hernandez-Leal et al ., 2016). One reason we do not compare against policy change literature is in our context, agent behaviours are not a series of static policies that are swapped between, but rather are continuous values that can change gradually or suddenly.…”

Section: Related Workmentioning

confidence: 99%

Improving trust and reputation assessment with dynamic behaviour

Player

Griffiths

2020

The Knowledge Engineering Review

View full text Add to dashboard Cite

Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time. Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour. In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

show abstract

Section: Related Workmentioning

confidence: 99%

Improving trust and reputation assessment with dynamic behaviour

Player

Griffiths

2020

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…They used a Markov Decision Process (MDP) algorithm as an opponent model learning mechanism, specifically the MDP-CL framework (Hernandez-Leal et al, 2014a). They, also, proposed an algorithm (2014b) that can use a priori information, in the form of a set of models, in order to promote faster detection of the opponent model; their algorithm also keeps a record of the models in case the opponent reuses his previous strategies.…”

Section: An Overview Of Background and Related Workmentioning

confidence: 99%

Synthetic learning agents in game-playing social environments

Kiourt

Kalles

2016

Adaptive Behavior

View full text Add to dashboard Cite

This paper investigates the performance of synthetic agents in playing and learning scenarios in a turn-based zero-sum game and highlights the ability of opponent-based learning models to demonstrate competitive playing performances in social environments. Synthetic agents are generated based on a variety of combinations of some key parameters, such as exploitation-vs-exploration trade-off, learning backup and discount rates, and speed of learning, and interact over a very large number of games on a grid infrastructure; experimental data is then analysed to generate clusters of agents that demonstrate interesting associations between eventual performance ranking and learning parameters' setup. The evolution of these clusters indicates that agents with a predisposition to knowledge exploration and slower learning tend to perform better than exploiters, which tend to prefer fast learning. Observing these clusters vis-à-vis the playing behaviours of the agents makes it also possible to investigate how to select opponents best from a group; initial results suggest that good progress and stable evolution arise when an agent faces opponents of increasing capacity, and that an agent with a good learning mechanism setup progresses better when it faces less favourably setup agents.

show abstract

Using a Priori Information for Fast Learning Against Non-stationary Opponents

Cited by 2 publications

References 7 publications

Improving trust and reputation assessment with dynamic behaviour

Improving trust and reputation assessment with dynamic behaviour

Synthetic learning agents in game-playing social environments

Contact Info

Product

Resources

About