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.

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.

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

scholarly journals Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery

2019 ◽  
Vol 33 ◽  
pp. 7651-7658 ◽  
Author(s):  
Hang Ma ◽  
Wolfgang Hönig ◽  
T. K. Satish Kumar ◽  
Nora Ayanian ◽  
Sven Koenig
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Single Agent ◽  
Pickup And Delivery ◽  
Combinatorial Search ◽  
Kinematic Constraints ◽  
Processing Step ◽  
Automated Warehouses ◽  
Online Setting ◽  
Multi Agent

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for wellformed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.

A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System

2016 ◽  
pp. 409-448 ◽  
Author(s):  
Kaushik Das Sharma
Keyword(s):  
Stochastic Optimization ◽  
State Feedback ◽  
Search Algorithm ◽  
Mimo System ◽  
Single Agent ◽  
The State ◽  
Multi Agent ◽  
Twin Rotor Mimo System ◽  
Twin Rotor ◽  
Feedback Regulator

Multi-agent optimization or population based search techniques are increasingly become popular compared to its single-agent counterpart. The single-agent gradient based search algorithms are very prone to be trapped in local optima and also the computational cost is higher. Multi-Agent Stochastic Optimization (MASO) algorithms are much powerful to overcome most of the drawbacks. This chapter presents a comparison of five MASO algorithms, namely genetic algorithm, particle swarm optimization, differential evolution, harmony search algorithm, and gravitational search algorithm. These MASO algorithms are utilized here to design the state feedback regulator for a Twin Rotor MIMO System (TRMS). TRMS is a multi-modal process and the design of its state feedback regulator is quite difficult using conventional methods available. MASO algorithms are typically suitable for such complex process optimizations. The performances of different MASO algorithms are presented and discussed in light of designing the state regulator for TRMS.

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 Decidability Results in First-Order Epistemic Planning

2020 ◽  
Author(s):  
Andrés Occhipinti Liberman ◽  
Rasmus Kræmmer Rendsvig
Keyword(s):  
Epistemic Logic ◽  
Single Agent ◽  
Higher Order ◽  
Dynamic Epistemic Logic ◽  
Existence Problem ◽  
Independent Action ◽  
First Order ◽  
Multi Agent ◽  
Action Representations ◽  
Multi Agent Planning

Propositional Dynamic Epistemic Logic (DEL) provides an expressive framework for epistemic planning, but lacks desirable features that are standard in first-order planning languages (such as problem-independent action representations via action schemas). A recent epistemic planning formalism based on First-Order Dynamic Epistemic Logic (FODEL) combines the strengths of DEL (higher-order epistemics) with those of first-order languages (lifted representation), yielding benefits in terms of expressiveness and representational succinctness. This paper studies the plan existence problem for FODEL planning, showing that while the problem is generally undecidable, the cases of single-agent planning and multi-agent planning with non-modal preconditions are decidable.

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

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