Distributed Constraint Optimization:Privacy Guarantees and Stochastic Uncertainty

Distributed Constraint Satisfaction (DisCSP) and Distributed Constraint Optimization (DCOP) are formal frameworks that can be used to model a variety of problems in which multiple decision-makers cooperate towards a common goal: from computing an equilibrium of a game, to vehicle routing problems, to combinatorial auctions. In this thesis, we independently address two important issues in such multi-agent problems: 1) how to provide strong guarantees on the protection of the privacy of the participants, and 2) how to anticipate future, uncontrollable events. On the privacy front, our contributions depart from previous work in two ways. First, we consider not only constraint privacy (the agents' private costs) and decision privacy (keeping the complete solution secret), but also two other types of privacy that have been largely overlooked in the literature: agent privacy, which has to do with protecting the identities of the participants, and topology privacy, which covers information about the agents' co-dependencies. Second, while previous work focused mainly on quantitatively measuring and reducing privacy loss, our algorithms provide stronger, qualitative guarantees on what information will remain secret. Our experiments show that it is possible to provide such privacy guarantees, while still scaling to much larger problems than the previous state of the art. When it comes to reasoning under uncertainty, we propose an extension to the DCOP framework, called DCOP under Stochastic Uncertainty (StochDCOP), which includes uncontrollable, random variables with known probability distributions that model uncertain, future events. The problem becomes one of making "optimal" offline decisions, before the true values of the random variables can be observed. We consider three possible concepts of optimality: minimizing the expected cost, minimizing the worst-case cost, or maximizing the probability of a-posteriori optimality. We propose a new family of StochDCOP algorithms, exploring the tradeoffs between solution quality, computational and message complexity, and privacy. In particular, we show how discovering and reasoning about co-dependencies on common random variables can yield higher-quality solutions

E[DPOP]: Distributed Constraint Optimization under Stochastic Uncertainty using Collaborative Sampling

Many applications that require distributed optimization also include uncertainty about the problem and the optimization criteria themselves. However, current approaches to distributed optimization assume that the problem is entirely known before optimization is carried out, while approaches to optimization with uncertainty have been investigated for centralized algorithms. This paper introduces the framework of Distributed Constraint Optimization under Stochastic Uncertainty (StochDCOP), in which random variables with known probability distributions are used to model sources of uncertainty. Our main novel contribution is a distributed procedure called col laborative sampling, which we use to produce several new versions of the DPOP algorithm for StochDCOPs. We evaluate the benefits of collaborative sampling over the simple approach in which each agent samples the random variables independently. We also show that collaborative sampling can be used to implement a new, distributed version of the consensus algorithm, which is a well-known algorithm for centralized, online stochastic optimization in which the solution chosen is the one that is optimal in most cases, rather than the one that maximizes the expected utility

FRODO 2.0: An Open-Source Framework for Distributed Constraint Optimization

Distributed Constraint Optimization (DCOP) is a field that has recently been getting more and more attention from academia and industry. However, very few open-source, off-the-shelf tools are currently available to solve DCOPs; examples are FRODO, DisChoco and DCOPolis. A DCOP platform should possess the following key qualities: the framework should be reliable and extensively tested, deployable in a truly distributed setting, and modular so that it is easy to customize and extend. This paper introduces the Java-based FRODO 2.0 framework, which possesses all three qualities. It is a complete re-design of the FRODO framework, released under the GNU Affero GPL license

Privacy Guarantees through Distributed Constraint Satisfaction

The reason for using distributed constraint satisfaction algorithms is often to allow agents to find a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this information. This paper describes some simple techniques that can be used with DisCSP algorithms such as DPOP, and provide sensible privacy guarantees based on the distributed solving process without sacrificing its efficiency

FRODO: An Open-Source Framework for Distributed Constraint Optimization

Distributed Constraint Optimization (DCOP) is a field that has recently been getting more and more attention from academia and industry. However, very few open-source, off-the-shelf tools are currently available to solve DCOPs; examples are FRODO, DisChoco and DCOPolis. A DCOP platform should possess the following key qualities: the framework should be reliable and extensively tested, deployable in a truly distributed setting, and modular so that it is easy to customize and extend. This poster presents the Java-based FRODO 2.0 framework, which possesses all three qualities. It is a complete re-design of the FRODO framework, released under the GNU Affero GPL license

Coordinating Agile Systems through the Model-based Execution of Temporal Plans

Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables. Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles. Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to “coach” the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plant’s state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan. Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed

Coordinating agile vehicles through the model-based execution of temporal plans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 149-155).Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables. Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.(cont.) Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to "coach" the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plant's state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan. Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem.(cont.) To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed.by Thomas Léauté.S.M

Coordinating Logistics Operations with Privacy Guarantees

Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferences private, thus precluding conventional negotiation. We show how AI techniques, in particular Distributed Constraint Optimization (DCOP), can be integrated with cryptographic techniques to allow such coordination without revealing agents' private information. The problem of assigning customers to companies is formulated as a DCOP, for which we propose two novel, privacy-preserving algorithms. We compare their performances and privacy properties on a set of Vehicle Routing Problem benchmarks