An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards

In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.Comment: 17 pages, 5 figure

Performance Evaluation - Annual Report Year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations. --Grid Computing

Agent-based transportation planning compared with scheduling heuristics

Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods

Mobile-Agent Planning in a Market-Oriented Environment

We propose a method for increasing incentives for sites to host arbitrary mobile agents in which mobile agents purchase their computing needs from host sites. We present a scalable market-based CPU allocation policy and an on-line algorithm that plans a mobile agent\u27s expenditure over a multihop ordered itinerary. The algorithm chooses a set of sites at which to execute and computational priorities at each site to minimize execution time while preserving a prespecified budget constraint. We present simulation results of our algorithm to show that our allocation policy and planning algorithm scale well as more agents are added to the system

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level

Hopscotch: Robust Multi-agent Search

The task of searching a space is critical to a wide range of diverse applications such as land mine clearing and planetary exploration. Because applications frequently require searching remote or hazardous locations, and because the task is easily divisible, it is natural to consider the use of multi-robot teams to accomplish the search task. An important topic of research in this area is the division of the task among robot agents. Interrelated with subtask assignment is failure handling, in the sense that, when an agent fails, its part of the task must then be performed by other agents. This thesis describes Hopscotch, a multi-agent search strategy that divides the search area into a grid of lots. Each agent is assigned responsibility to search one lot at a time, and upon completing the search of that lot the agent is assigned a new lot. Assignment occurs in real time using a simple contract net. Because lots that have been previously searched are skipped, the order of search from the point of view of a particular agent is reminiscent of the progression of steps in the playground game of Hopscotch. Decomposition of the search area is a common approach to multi-agent search, and auction-based contract net strategies have appeared in recent literature as a method of task allocation in multi-agent systems. The Hopscotch strategy combines the two, with a strong focus on robust tolerance of agent failures. Contract nets typically divide all known tasks among available resources. In contrast, Hopscotch limits each agent to one assigned lot at a time, so that failure of an agent compels re-allocation of only one lot search task. Furthermore, the contract net is implemented in an unconventional manner that empowers each agent with responsibility for contract management. This novel combination of real-time assignment and decentralized management allows Hopscotch to resiliently cope with agent failures. The Hopscotch strategy was modeled and compared to other multi-agent strate- gies that tackle the search task in a variety of ways. Simulation results show that Hopscotch is failure-tolerant and very effective in comparison to the other approaches in terms of both search time and search efficiency. Although the search task modeled here is a basic one, results from simulations show the promise of using this strategy for more complicated scenarios, and with actual robot agents

Robust distributed planning strategies for autonomous multi-agent teams

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from department-submitted PDF version of thesis. This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 225-244).The increased use of autonomous robotic agents, such as unmanned aerial vehicles (UAVs) and ground rovers, for complex missions has motivated the development of autonomous task allocation and planning methods that ensure spatial and temporal coordination for teams of cooperating agents. The basic problem can be formulated as a combinatorial optimization (mixed-integer program) involving nonlinear and time-varying system dynamics. For most problems of interest, optimal solution methods are computationally intractable (NP-Hard), and centralized planning approaches, which usually require high bandwidth connections with a ground station (e.g. to transmit received sensor data, and to dispense agent plans), are resource intensive and react slowly to local changes in dynamic environments. Distributed approximate algorithms, where agents plan individually and coordinate with each other locally through consensus protocols, can alleviate many of these issues and have been successfully used to develop real-time conflict-free solutions for heterogeneous networked teams. An important issue associated with autonomous planning is that many of the algorithms rely on underlying system models and parameters which are often subject to uncertainty. This uncertainty can result from many sources including: inaccurate modeling due to simplifications, assumptions, and/or parameter errors; fundamentally nondeterministic processes (e.g. sensor readings, stochastic dynamics); and dynamic local information changes. As discrepancies between the planner models and the actual system dynamics increase, mission performance typically degrades. The impact of these discrepancies on the overall quality of the plan is usually hard to quantify in advance due to nonlinear effects, coupling between tasks and agents, and interdependencies between system constraints. However, if uncertainty models of planning parameters are available, they can be leveraged to create robust plans that explicitly hedge against the inherent uncertainty given allowable risk thresholds. This thesis presents real-time robust distributed planning strategies that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. One class of distributed combinatorial planning algorithms involves using auction algorithms augmented with consensus protocols to allocate tasks amongst a team of agents while resolving conflicting assignments locally between the agents. A particular algorithm in this class is the Consensus-Based Bundle Algorithm (CBBA), a distributed auction protocol that guarantees conflict-free solutions despite inconsistencies in situational awareness across the team. CBBA runs in polynomial time, demonstrating good scalability with increasing numbers of agents and tasks. This thesis builds upon the CBBA framework to address many realistic considerations associated with planning for networked teams, including time-critical mission constraints, limited communication between agents, and stochastic operating environments. A particular focus of this work is a robust extension to CBBA that handles distributed planning in stochastic environments given probabilistic parameter models and different stochastic metrics. The Robust CBBA algorithm proposed in this thesis provides a distributed real-time framework which can leverage different stochastic metrics to hedge against parameter uncertainty. In mission scenarios where low probability of failure is required, a chance-constrained stochastic metric can be used to provide probabilistic guarantees on achievable mission performance given allowable risk thresholds. This thesis proposes a distributed chance-constrained approximation that can be used within the Robust CBBA framework, and derives constraints on individual risk allocations to guarantee equivalence between the centralized chance-constrained optimization and the distributed approximation. Different risk allocation strategies for homogeneous and heterogeneous teams are proposed that approximate the agent and mission score distributions a priori, and results are provided showing improved performance in time-critical mission scenarios given allowable risk thresholds.by Sameera S. Ponda.Ph.D

Performance evaluation - annual report year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations