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Distributed problem solving
 AI Magazine
, 2012
"... Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives ..."
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Cited by 17 (13 self)
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Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives have been applied such that the individual agent rewards are aligned with the team reward. We illustrate the motivations for distributed problem solving with an example. Imagine a decentralized channelallocation problem in a wireless local area network (WLAN), where each access point (agent) in the WLAN needs to allocate itself a channel to broadcast such that no two access points with overlapping broadcast regions (neighboring agents) are allocated the same channel to avoid interference. Figure 1 shows example mobile WLAN access points, where each access point is a Create robot fitted with a wireless CenGen radio card. Figure 2a shows an illustration of such a problem with three access points in a WLAN, where each oval ring represents the broadcast region of an access point. This problem can, in principle, be solved with a centralized approach by having each and every agent transmit all the relevant information, that is, the set of possible channels that the agent can allocate itself and its set of neighboring agents, to a centralized server. However, this centralized approach may incur unnecessary communication cost compared to a distrib
Evaluation of CBR on Live Networks
"... Abstract. A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses ..."
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Cited by 6 (4 self)
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Abstract. A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis—a framework for comparing and deploying DCOP software in heterogeneous environments—to contribute an analysis of two stateoftheart DCOP algorithms run in various network environments solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cyclebased runtime and concurrent constraint checks. 1
Ensuring Privacy through Distributed Computation in MultipleDepot Vehicle Routing Problems
"... Abstract. The Vehicle Routing Problem (VRP) has been extensively studied over the last twenty years, because it is an abstraction of many reallife logistics problems. In its multipledepot variant (MDVRP), the routes of vehicles located at various depots must be optimized to serve a number of custo ..."
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Abstract. The Vehicle Routing Problem (VRP) has been extensively studied over the last twenty years, because it is an abstraction of many reallife logistics problems. In its multipledepot variant (MDVRP), the routes of vehicles located at various depots must be optimized to serve a number of customers. In this paper, we investigate how to protect the privacy of delivery companies, when each depot is owned by a different company with a limited view of the overall problem. Companies then need to exchange messages with each other to coordinate the assignment of customers to depots. We show how Distributed Constraint Optimization (DCOP) can be used to solve the assignment problem using distributed computation, and we study the guarantees that can be provided with respect to the protection of each company’s knowledge about the problem. 1
Dynamic DFS Tree in ADOPTing
, 2007
"... Several distributed constraint reasoning algorithms employ Depth First Search (DFS) trees on the constraint graph that spans involved agents. In this article we show that it is possible to dynamically detect a minimal DFS tree, compatible with the current order on agents, during the distributed cons ..."
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Cited by 4 (2 self)
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Several distributed constraint reasoning algorithms employ Depth First Search (DFS) trees on the constraint graph that spans involved agents. In this article we show that it is possible to dynamically detect a minimal DFS tree, compatible with the current order on agents, during the distributed constraint reasoning process of the ADOPT algorithm. This also allows for shorter DFS trees during the initial steps of the algorithm, while some constraints did not yet prove useful given visited combinations of assignments. Earlier distributed algorithms for finding spanning trees on agents did not look to maintain compatibility with an order already used. We also show that announcing a nogood to a single optional agent is bringing significant improvements in the total number of messages. The dynamic detection of the DFS tree brings improvements in simulated time. 1
Stochastic dominance in stochastic DCOPs for risksensitive applications
 In Proceedings of AAMAS
, 2012
"... Distributed constraint optimization problems (DCOPs) are wellsuited for modeling multiagent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers ..."
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Distributed constraint optimization problems (DCOPs) are wellsuited for modeling multiagent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this paper, we make three contributions: (1) we propose a stricter objective for SDCOPs, namely to find a solution with the most stochastically dominating probability distribution reward function; (2) we introduce an algorithm to find such solutions; and (3) we show that stochastically dominating solutions can indeed be less risky than expected reward maximizing solutions.
Recent advances in dynamic, distributed constraint optimization
 Swiss Federal Institute of Technology (EPFL), Lausanne (Switzerland
, 2006
"... Many new technologies like sensor networks, RFID and the internet provide companies with a wealth of interconnected, realtime data sources and have significant potential for automating certain parts of their business processes. The potential of these technologies goes well beyond the limited role o ..."
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Cited by 1 (0 self)
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Many new technologies like sensor networks, RFID and the internet provide companies with a wealth of interconnected, realtime data sources and have significant potential for automating certain parts of their business processes. The potential of these technologies goes well beyond the limited role of simply collecting, storing and sending data to a central location for processing. We believe that these ”smart items ” will certainly evolve to more complex embedded systems that can also communicate between themselves, aggregate data and cooperate to take optimal decisions locally, with little or no influence from a central system. We believe that many key enabling technologies will come from the field of Distributed Artificial Intelligence. Specifically, within that field, Distributed Constraint Optimization is an excellent formalism for problem solving in multiagent systems. Many reallife problems like planning, scheduling and resource allocation can be modeled in this framework, and effectively solved in a distributed fashion. This paper is a brief introduction to this area. We present some important issues that arise in this domain, and some of our recent results.
Analyzing the Performance of Distributed Algorithms
"... Abstract — A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses ..."
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Abstract — A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis—a framework for comparing and deploying DCOP software in heterogeneous environments—to contribute an analysis of two stateoftheart DCOP algorithms solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cyclebased runtime and concurrent constraint checks. I.
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"... Abstract—As humanagent teams get increasingly deployed in the realworld, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of ..."
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Abstract—As humanagent teams get increasingly deployed in the realworld, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these problems, the agents and humans optimize their constraints in a distributed manner. This paper makes two key contributions: (a) Proves theoretical properties of the algorithm used (named DSA) for solving distributed constraint optimization problems, which
Effect of human biases on humanagent teams
"... Abstract—As humanagent teams get increasingly deployed in the realworld, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of ..."
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Abstract—As humanagent teams get increasingly deployed in the realworld, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these problems, the agents and humans optimize their constraints in a distributed manner. This paper makes two key contributions: (a) Proves theoretical properties of the algorithm used (named DSA) for solving distributed constraint optimization problems, which
Under consideration for publication in Theory and Practice of Logic Programming 1 Logic and Constraint Logic Programming for Distributed Constraint Optimization
, 2003
"... The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e.g., multiagent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized mann ..."
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The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e.g., multiagent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized manner. The state of the art in solving DCOPs relies on the use of adhoc infrastructures and adhoc constraint solving procedures. This paper investigates an infrastructure for solving DCOPs that is completely built on logic programming technologies. In particular, the paper explores the use of a general constraint solver (a constraint logic programming system in this context) to handle the agentlevel constraint solving. The preliminary experiments show that logic programming provides benefits over a stateoftheart DCOP system, in terms of performance and scalability, opening the doors to the use of more advanced technology (e.g., search strategies and complex constraints) for solving DCOPs.