Task allocation using a team or coalition of robots is one of the most
important problems in robotics, computer science, operational research, and
artificial intelligence. In recent work, research has focused on handling
complex objectives and feasibility constraints amongst other variations of the
multi-robot task allocation problem. There are many examples of important
research progress in these directions. We present a general formulation of the
task allocation problem that generalizes several versions that are
well-studied. Our formulation includes the states of robots, tasks, and the
surrounding environment in which they operate. We describe how the problem can
vary depending on the feasibility constraints, objective functions, and the
level of dynamically changing information. In addition, we discuss existing
solution approaches for the problem including optimization-based approaches,
and market-based approaches.

本文介绍一个通用的、广泛涉及机器人、计算机科学、运筹学和人工智能等领域的任务分配问题，包括机器人、任务和周围环境状态的多个版本，并探讨了解决该问题的现有方法，包括基于优化和市场的方法。

使用机器人团队的任务分配

Task Allocation using a Team of Robots

Multi-objective optimization is a crucial matter in computer systems design
space exploration because real-world applications often rely on a trade-off
between several objectives. Derivatives are usually not available or
impractical to compute and the feasibility of an experiment can not always be
determined in advance. These problems are particularly difficult when the
feasible region is relatively small, and it may be prohibitive to even find a
feasible experiment, let alone an optimal one.
We introduce a new methodology and corresponding software framework,
HyperMapper 2.0, which handles multi-objective optimization, unknown
feasibility constraints, and categorical/ordinal variables. This new
methodology also supports injection of the user prior knowledge in the search
when available. All of these features are common requirements in computer
systems but rarely exposed in existing design space exploration systems. The
proposed methodology follows a white-box model which is simple to understand
and interpret (unlike, for example, neural networks) and can be used by the
user to better understand the results of the automatic search.
We apply and evaluate the new methodology to the automatic static tuning of
hardware accelerators within the recently introduced Spatial programming
language, with minimization of design run-time and compute logic under the
constraint of the design fitting in a target field-programmable gate array
chip. Our results show that HyperMapper 2.0 provides better Pareto fronts
compared to state-of-the-art baselines, with better or competitive hypervolume
indicator and with 8x improvement in sampling budget for most of the benchmarks
explored.

HyperMapper 2.0 is a new methodology and software framework that handles multi-objective optimization, unknown feasibility constraints, and categorical/ordinal variables for computer systems design, achieving better Pareto fronts and improved sampling for hardware accelerators.