16 research outputs found
Belief and Truth in Hypothesised Behaviours
There is a long history in game theory on the topic of Bayesian or "rational"
learning, in which each player maintains beliefs over a set of alternative
behaviours, or types, for the other players. This idea has gained increasing
interest in the artificial intelligence (AI) community, where it is used as a
method to control a single agent in a system composed of multiple agents with
unknown behaviours. The idea is to hypothesise a set of types, each specifying
a possible behaviour for the other agents, and to plan our own actions with
respect to those types which we believe are most likely, given the observed
actions of the agents. The game theory literature studies this idea primarily
in the context of equilibrium attainment. In contrast, many AI applications
have a focus on task completion and payoff maximisation. With this perspective
in mind, we identify and address a spectrum of questions pertaining to belief
and truth in hypothesised types. We formulate three basic ways to incorporate
evidence into posterior beliefs and show when the resulting beliefs are
correct, and when they may fail to be correct. Moreover, we demonstrate that
prior beliefs can have a significant impact on our ability to maximise payoffs
in the long-term, and that they can be computed automatically with consistent
performance effects. Furthermore, we analyse the conditions under which we are
able complete our task optimally, despite inaccuracies in the hypothesised
types. Finally, we show how the correctness of hypothesised types can be
ascertained during the interaction via an automated statistical analysis.Comment: 44 pages; final manuscript published in Artificial Intelligence (AIJ
Courtesy as a Means to Coordinate
We investigate the problem of multi-agent coordination under rationality
constraints. Specifically, role allocation, task assignment, resource
allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY)
that enables fast convergence to efficient and fair allocations based on a
simple convention of courtesy. We prove that following such convention induces
a strategy which constitutes an -subgame-perfect equilibrium of the
repeated allocation game with discounting. Simulation results highlight the
effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms,
since it achieves more than two orders of magnitude faster convergence, higher
efficiency, fairness, and average payoff.Comment: Accepted at AAMAS 2019 (International Conference on Autonomous Agents
and Multiagent Systems
Recommended from our members
Ad-hoc teamwork with behavior-switching agents
As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being able to coordinate in advance. This kind of cooperation, in which agents have to learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be guaranteed in many real-world settings. In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task. This adds complexity to the planning problem as now an agent needs to recognize that a change has occurred in addition to figuring out what is the new type of the teammate it is interacting with. In this paper, we present a novel Convolutional-Neural-Network-based Change Point Detection (CPD) algorithm for ad hoc teamwork. When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms.Electrical and Computer Engineerin