## Publications

Filter tags (click to remove): UAI

### 2015

Stefano V. Albrecht, Subramanian Ramamoorthy

**Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models**

Conference on Uncertainty in Artificial Intelligence (UAI), 2015

Abstract | BibTex | arXiv

UAIagent-modelling

**Abstract:**The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there exist several methods for the construction of a behavioural hypothesis, there is currently no universal theory which would allow an agent to contemplate the correctness of a hypothesis. In this work, we present a novel algorithm which decides this question in the form of a frequentist hypothesis test. The algorithm allows for multiple metrics in the construction of the test statistic and learns its distribution during the interaction process, with asymptotic correctness guarantees. We present results from a comprehensive set of experiments, demonstrating that the algorithm achieves high accuracy and scalability at low computational costs.

```
@inproceedings{ albrecht2015criticising,
title = {Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence},
pages = {52--61},
year = {2015}
}
```

### 2014

Stefano V. Albrecht, Subramanian Ramamoorthy

**On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems**

Conference on Uncertainty in Artificial Intelligence (UAI), 2014

Abstract | BibTex | arXiv | Appendix

UAIagent-modelling

**Abstract:**While many multiagent algorithms are designed for homogeneous systems (i.e. all agents are identical), there are important applications which require an agent to coordinate its actions without knowing a priori how the other agents behave. One method to make this problem feasible is to assume that the other agents draw their latent policy (or type) from a specific set, and that a domain expert could provide a specification of this set, albeit only a partially correct one. Algorithms have been proposed by several researchers to compute posterior beliefs over such policy libraries, which can then be used to determine optimal actions. In this paper, we provide theoretical guidance on two central design parameters of this method: Firstly, it is important that the user choose a posterior which can learn the true distribution of latent types, as otherwise suboptimal actions may be chosen. We analyse convergence properties of two existing posterior formulations and propose a new posterior which can learn correlated distributions. Secondly, since the types are provided by an expert, they may be inaccurate in the sense that they do not predict the agentsâ€™ observed actions. We provide a novel characterisation of optimality which allows experts to use efficient model checking algorithms to verify optimality of types.

```
@inproceedings{ albrecht2014convergence,
title = {On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence},
pages = {12--21},
year = {2014}
}
```