In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
Potential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision– making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving Dec–POMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving Dec–POMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for Dec–POMDPs, and demonstrate our new algorithm’s performance in a box pushing problem. Our results show that we consistently outperform the state–of–the–art Dec– POMDP solver by an average of 100% across the range of noise in the observation function.

Ex Output:
Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs


Ex Input:
We study the problem of identifying the best action among a set of possible options when the value of each action is given by a mapping from a number of noisy micro-observables in the so-called fixed confidence setting. Our main motivation is the application to the minimax game search, which has been a major topic of interest in artificial intelligence. In this paper we introduce an abstract setting to clearly describe the essential properties of the problem. While previous work only considered a two-move game tree search problem, our abstract setting can be applied to the general minimax games where the depth can be non-uniform and arbitrary, and transpositions are allowed. We introduce a new algorithm (LUCB-micro) for the abstract setting, and give its lower and upper sample complexity results. Our bounds recover some previous results, which were only available in more limited settings, while they also shed further light on how the structure of minimax problems influence sample complexity.

Ex Output:
Structured Best Arm Identification with Fixed Confidence


Ex Input:
This paper presents an ontology-based approach for the design of a collaborative business process model (CBP). This CBP is considered as a specification of needs in order to build a collaboration information system (CIS) for a network of organisations. The study is a part of a model driven engineering approach of the CIS in a specific enterprise interoperability framework that will be summarised. An adaptation of the Business Process Modeling Notation (BPMN) is used to represent the CBP model. We develop a knowledge-based system (KbS) which is composed of three main parts: knowledge gathering, knowledge representation and reasoning, and collaborative business process modelling. The first part starts from a high abstraction level where knowledge from business partners is captured. A collaboration ontology is defined in order to provide a structure to store and use the knowledge captured. In parallel, we try to reuse generic existing knowledge about business processes from the MIT Process Handbook repository. This results in a collaboration process ontology that is also described. A set of rules is defined in order to extract knowledge about fragments of the CBP model from the two previous ontologies. These fragments are finally assembled in the third part of the KbS. A prototype of the KbS has been developed in order to implement and support this approach. The prototype is a computer-aided design tool of the CBP. In this paper, we will present the theoretical aspects of each part of this KbS as well as the tools that we developed and used in order to support its functionalities.

Ex Output:

Knowledge-based system for collaborative process specification