The Trust-Based Interactive Partially Observable Markov Decision Process

Cooperative agent and robot systems are designed so that each is working toward the same common good. The problem is that the software systems are extremely complex and can be subverted by an adversary to either break the system or potentially worse, create sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. This research focuses on the ability of a group of agents to reason about the trustworthiness of each other and make decisions about whether to cooperate. A trust-based interactive partially observable Markov decision process (TI-POMDP) is developed to model the trust interactions between agents, enabling the agents to select the best course of action from the current state. The TI-POMDP is a novel approach to multiagent cooperation based on an interactive partially observable Markov decision process (I-POMDP) augmented with trust relationships. Experiments using the Defender simulation demonstrate the TI-POMDP\u27s ability to accurately track the trust levels of agents with hidden agendas The TI-POMDP provides agents with the information needed to make decisions based on their level of trust and model of the environment. Testing demonstrates that agents quickly identify the hidden trust levels and mitigate the impact of a deceitful agent in comparison with a trust vector model. Agents using the TI-POMDP model achieved 3.8 times the average reward of agents using a trust vector model

Learning Initial Trust among Interacting Agents

Abstract. Trust learning is a crucial aspect of information exchange, negotiation, and any other kind of social interaction among autonomous agents in open systems. But most current probabilistic models for computational trust learning lack the ability to take context into account when trying to predict future behavior of interacting agents. Moreover, they are not able to transfer knowledge gained in a specific context to a related context. Humans, by contrast, have proven to be especially skilled in perceiving traits like trustworthiness in such so-called initial trust situations. The same restriction applies to most multiagent learning problems. In complex scenarios most algorithms do not scale well to large state-spaces and need numerous interactions to learn. We argue that trust related scenarios are best represented in a system of relations to capture semantic knowledge. Following recent work on nonparametric Bayesian models we propose a flexible and context sensitive way to model and learn multidimensional trust values which is particularly well suited to establish trust among strangers without prior relationship. To evaluate our approach we extend a multiagent framework by allowing agents to break an agreed interaction outcome retrospectively. The results suggest that the inherent ability to discover clusters and relationships between clusters that are best supported by the data allows to make predictions about future behavior of agents especially when initial trust is involved