Opening the black box of trust: reasoning about trust models in a BDI agent

A. Koster; M. Schorlemmer; J. Sabater-Mir

doi:10.1093/logcom/exs003

Opening the black box of trust: reasoning about trust models in a BDI agent

Journal of Logic and Computation ◽

10.1093/logcom/exs003 ◽

2012 ◽

Vol 23 (1) ◽

pp. 25-58 ◽

Cited By ~ 14

Author(s):

A. Koster ◽

M. Schorlemmer ◽

J. Sabater-Mir

Keyword(s):

Black Box ◽

Bdi Agent ◽

Trust Models ◽

Trust Reasoning

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Multi-Agent Intention Progression with Black-Box Agents

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/19 ◽

2021 ◽

Author(s):

Michael Dann ◽

Yuan Yao ◽

Brian Logan ◽

John Thangarajah

Keyword(s):

Monte Carlo ◽

Black Box ◽

Tree Search ◽

New Approach ◽

Monte Carlo Tree Search ◽

Partially Ordered ◽

Black Boxes ◽

Bdi Agent ◽

Multi Agent

We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves.

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