scholarly journals Advances and Challenges in Privacy Preserving Planning

2018 ◽  
Author(s):  
Guy Shani
Keyword(s):  
Private Information ◽  
Planning Process ◽  
Privacy Preserving ◽  
Search Process ◽  
Local Planning ◽  
Open Problems ◽  
Future Directions ◽  
Multi Agent ◽  
Global Coordination ◽  
Multi Agent Planning

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.

scholarly journals Distributed Heuristic Forward Search for Multi-agent Planning

2014 ◽  
Vol 51 ◽  
pp. 293-332 ◽  
Author(s):  
R. Nissim ◽  
R. Brafman
Keyword(s):  
Partial Order ◽  
Private Information ◽  
Search Algorithm ◽  
Single Agent ◽  
Privacy Preserving ◽  
Forward Search ◽  
Planning Techniques ◽  
State Expansion ◽  
Multi Agent ◽  
Multi Agent Planning

This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem -- one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient -- outperforming previous algorithms by orders of magnitude -- while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the A* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.

Action dependencies in privacy-preserving multi-agent planning

2018 ◽  
Vol 32 (6) ◽  
pp. 779-821
Author(s):  
Shlomi Maliah ◽  
Guy Shani ◽  
Roni Stern
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

Collaborative privacy preserving multi-agent planning

2016 ◽  
Vol 31 (3) ◽  
pp. 493-530 ◽  
Author(s):  
Shlomi Maliah ◽  
Guy Shani ◽  
Roni Stern
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

2021 ◽  
Vol 10 (2) ◽  
pp. 33
Author(s):  
Rafael C. Cardoso ◽  
Angelo Ferrando ◽  
Daniela Briola ◽  
Claudio Menghi ◽  
Tobias Ahlbrecht
Keyword(s):  
Research Trends ◽  
Multi Agent Systems ◽  
Open Problems ◽  
Future Directions ◽  
Agent Systems ◽  
Research Areas ◽  
Multi Agent ◽  
Active Research ◽  
High Level ◽  
First Time

Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.

Coordination in Multi-Agent Planning with an Application in Logistics

2005 ◽  
pp. 194-224 ◽  
Author(s):  
Jeroen Valk ◽  
Mathijs de Weerdt ◽  
Cees Witteveen
Keyword(s):  
Polynomial Time ◽  
Planning Process ◽  
Time Algorithm ◽  
Clear View ◽  
Merging Algorithm ◽  
Multi Agent ◽  
Multi Agent Planning

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.

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

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