We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately, in these models optimal planning is provably intractable. By communicating their local observations before they take actions, agents synchronize their knowledge of the environment, and the planning problem reduces to...

Market-based algorithms have become popular in collaborative multi-agent planning due to their simplicity, distributedness, low communication requirements, and proven success in domains such as task allocation and robotic exploration. Most existing marketbased algorithms, however, suffer from two main drawbacks: resource prices must be carefully handcrafted for each problem domain, and there is...

The oil industry is increasingly interested in improving the planning of their operations due to the dynamic nature of this business. Decisions made at the oil chain differ in the activity range (spatial integration) and planning horizon (temporal integration). This paper purpose is to address the spatial integration under uncertainty in the oil chain at the tactical planning level and proposes...

Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tac...

Market-based algorithms have become popular in collaborative multi-agent planning due to their simplicity, distributedness, low communication requirements, and proven success in domains such as task allocation and robotic exploration. Most existing marketbased algorithms, however, suffer from two main drawbacks: resource prices must be carefully handcrafted for each problem domain, and there is...

Transforming a planning instance into a propositional formula φ to be solved by a SAT solver is a common approach in AI planning. In the context of multiagent planning, this approach causes the distributed SAT problem: given φ distributed among agents –each agent knows a part of φ but no agent knows the whole φ–, check if φ is SAT or UNSAT by message passing. On the other hand, Asynchronous Bac...

High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches [3]. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized plan...

This paper investigates the centralized resource allocation with centralized structures by using the data envelopment analysis-ratio (DEA-R) models. To this end, it proposes a method to determine the resource allocation of centralized structures such that the ratio of inputs to outputs are minimized.

This dissertation explores the challenges of one of the most difficult classes of real-world tasks for multirobot teams: those that require long-term planning of tightly-coordinated actions between teammates. These tasks involve solving a distributed multi-agent planning problem in which the actions of robots are tightly coupled. Moreover, because of uncertainty in the environment and the team,...

Introduction. Automated planning is a central area of Artificial Intelligence which aims to design a powerful deliberation layer for autonomous intelligent systems. Autonomy of intelligent systems doesn’t concern only planning and deliberation but also acting. These two aspects are not completely disjoint: actors may deliberate or plan both before and during acting in order to perform intellige...