Coordination in Multi-Agent Planning with an Application in Logistics

Author(s):  
Jeroen Valk ◽  
Mathijs de Weerdt ◽  
Cees Witteveen

Multi-agent planning comprises planning in an environment with multiple autonomous actors. Techniques for multi-agent planning differ from conventional planning in that planning activities are distributed and the planning autonomy of the agents must be respected. We focus on approaches to coordinate the multi-agent planning process. While usually coordination is intertwined with the planning process, we distinguish a number of separate phases in the planning process to get a clear view on the different role(s) of coordination. In particular, we discuss the pre-planning coordination phase and post-planning coordination phase. In the pre-planning part, we view coordination as the process of managing (sub) task dependencies and we discuss a method that ensures complete planning autonomy by introducing additional (intra-agent) dependencies. In the post-planning part, we will show how agents can improve their plans through the exchange of resources. We present a plan merging algorithm that uses these resources to reduce the costs of independently developed plans. This (any-time) algorithm runs in polynomial time.

Author(s):  
Guy Shani

Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. CPPP has gained attention in recent years as an important sub area of multi agent planning, presenting new challenges to the planning community. In this paper we describe recent advancements, and outline open problems and future directions in this field. We begin with describing different models of privacy, such as weak and strong privacy, agent privacy, and cardinality preserving privacy. We then discuss different solution approaches, focusing on the two prominent methods --- joint creation of a global coordination scheme first, followed by independent planning to extend the global scheme with private actions; and collaborative local planning where agents communicate information concerning their planning process. In both cases a heuristic is needed to guide the search process. We describe several adaptations of well known classical planning heuristic to CPPP, focusing on the difficulties in computing the heuristic without disclosing private information.


10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.


Sign in / Sign up

Export Citation Format

Share Document