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Building Multirobot Coalitions Through Automated Task Solution Synthesis -- A group of robots can move to, or push boxes to, specified locations by sharing information when individual robots cannot perform the tasks separately
, 2006
"... This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow ..."
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Cited by 56 (16 self)
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This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multirobot behaviors for accomplishing a multirobot task. We present the centralized anytime ASyMTRe configuration algorithm, proving that the algorithm is correct, and formally addressing issues of completeness and optimality. We then present a distributed version of ASyMTRe, called ASyMTRe-D, which uses communication to enable distributed coalition formation. We validate the centralized approach by applying the ASyMTRe methodology to two application scenarios: multirobot transportation and multirobot box pushing. We then validate the ASyMTRe-D implementation in the multirobot transportation task, illustrating its fault-tolerance capabilities. The advantages of this new approach are that it: 1) enables robots to synthesize new task solutions using fundamentally different combinations of sensors and effectors for different coalition compositions and 2) provides a general mechanism for sharing sensory information across networked robots.
A Survey of Algorithms for Real-Time Bayesian Network Inference
- In In the joint AAAI-02/KDD-02/UAI-02 workshop on Real-Time Decision Support and Diagnosis Systems
, 2002
"... As Bayesian networks are applied to more complex and realistic real-world applications, the development of more efficient inference algorithms working under real-time constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network ..."
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Cited by 46 (2 self)
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As Bayesian networks are applied to more complex and realistic real-world applications, the development of more efficient inference algorithms working under real-time constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network inference algorithms. In particular, previous research on real-time inference is reviewed. It provides a framework for understanding these algorithms and the relationships between them. Some important issues in real-time Bayesian networks inference are also discussed.
Extending Multi-Agent Cooperation by Overhearing
, 2001
"... Much cooperation among humans happens following a common pattern: by chance or deliberately, a person overhears a conversation between two or more parties and steps in to help, for instance by suggesting answers to questions, by volunteering to perform actions, by making observations or adding infor ..."
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Cited by 35 (13 self)
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Much cooperation among humans happens following a common pattern: by chance or deliberately, a person overhears a conversation between two or more parties and steps in to help, for instance by suggesting answers to questions, by volunteering to perform actions, by making observations or adding information. We describe an abstract architecture to support a similar pattern in societies of artificial agents. Our architecture involves pairs of so-called service agents (or services) engaged in some tasks, and unlimited number of suggestive agents (or suggesters). The latter
Design-to-Criteria Scheduling: Real-Time Agent Control
- Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems, LNCS
, 2000
"... Design-to-Criteria builds custom schedules for agents that meet hard temporal constraints, hard resource constraints, and soft constraints stemming from soft task interactions or soft commitments made with other agents. Design-to-Criteria is designed specifically for online application -- it cop ..."
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Cited by 34 (17 self)
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Design-to-Criteria builds custom schedules for agents that meet hard temporal constraints, hard resource constraints, and soft constraints stemming from soft task interactions or soft commitments made with other agents. Design-to-Criteria is designed specifically for online application -- it copes with exponential combinatorics to produce these custom schedules in a resource bounded fashion. This enables agents to respond to changes in problem solving or the environment as they arise. Introduction Complex autonomous agents operating in open, dynamic environments must be able to address deadlines and resource limitations in their problem solving. This is partly due to characteristics of the environment, and partly due to the complexity of the applications typically handled by software agents in our research. In open environments, requests for service can arrive at the local agent at any time, thus making it difficult to fully plan or predict the agent's future workload. In dyn...
A survey of point-based POMDP solvers
- AUTON AGENT MULTI-AGENT SYST
, 2012
"... The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly ..."
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Cited by 33 (5 self)
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The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly due to the idea of restricting value function computations to a finite subset of the belief space, permitting only local value updates for this subset. This approach, known as point-based value iteration, avoids the exponential growth of the value function, and is thus applicable for domains with longer horizons, even with relatively large state spaces. Many extensions were suggested to this basic idea, focusing on various aspects of the algorithm—mainly the selection of the belief space subset, and the order of value function updates. In this survey, we walk the reader through the fundamentals of point-based value iteration, explaining the main concepts and ideas. Then, we survey the major extensions to the basic algorithm, discussing their merits. Finally, we include an extensive empirical analysis using well known benchmarks, in order to shed light on the strengths and limitations of the various approaches.
Implementing Soft Real-Time Agent Control
, 2001
"... Real-time control has become increasingly important as technologies are moved from the lab into real world situations or physical simulations. The complexity associated with these systems increases as control and autonomy are distributed, due to such issues as precedence constraints, shared resource ..."
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Cited by 31 (11 self)
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Real-time control has become increasingly important as technologies are moved from the lab into real world situations or physical simulations. The complexity associated with these systems increases as control and autonomy are distributed, due to such issues as precedence constraints, shared resources, and the lack of a complete and consistent world view. In this paper we describe a realtime environment requiring distributed control, and how we modified our existing multi-agent technologies to meet this need. Two types of enhancements are covered: those which enable planning to meet real-time constraints, such as our task representation, metalevel costing, alternative plan selection, and partial-order scheduling, and those which facilitate on-line real-time control, including scheduling flexibility, caching, and windowed commitments.
The Dynamic Selection of Coordination Mechanisms
- Journal of Autonomous Agents and Multi-Agent Systems
, 2004
"... This paper presents and evaluates a decision making framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of something that is imposed u ..."
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Cited by 25 (2 self)
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This paper presents and evaluates a decision making framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of something that is imposed upon the system at design time, to something that the agents select at run-time in order to fit their prevailing circumstances and their current coordination needs. Using this framework, agents make informed choices about when and how to coordinate and when to respond to requests for coordination. The framework is empirically evaluated, in a grid world scenario, and we highlight those types of environments in which it is e#ective.
Anytime local search for distributed constraint optimization
- In Twenty-Third AAAI Conference on Artificial Intelligence
, 2008
"... Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between th ..."
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Cited by 24 (6 self)
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Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between the global evaluation of a system’s state and the private evaluation of states by agents, agents are unaware of the global best state which is explored by the algorithm. Previous attempts to use local search algorithms for solving DisCOPs reported the state held by the system at the termination of the algorithm, which was not necessarily the best state explored. A general framework for implementing distributed local search algorithms for DisCOPs is proposed. The proposed framework makes use of a BF S-tree in order to accumulate the costs of the system’s state in its different steps and to propagate the detection of a new best step when it is found. The resulting framework enhances local search algorithms for DisCOPs with the anytime property. The proposed framework does not require additional network load. Agents are required to hold a small (linear) additional space (beside the requirements of the algorithm in use). The proposed framework preserves privacy at a higher level than complete DisCOP algorithms which make use of a pseudo-tree (ADOP T, DP OP).
The Control of Reasoning in Resource-Bounded Agents
- The Knowledge Engineering Review
, 2001
"... Autonomous agents are systems capable of autonomous decision making in real-time environments. Computation is a valuable resource for such decision making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a me ..."
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Cited by 22 (1 self)
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Autonomous agents are systems capable of autonomous decision making in real-time environments. Computation is a valuable resource for such decision making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper, we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each. 1
SpeedBoost: Anytime Prediction with Uniform Near-Optimality
"... We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by selecting from a set of simpler candidate predictors. These anytime predictors not only generate approximate predictions r ..."
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Cited by 19 (2 self)
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We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by selecting from a set of simpler candidate predictors. These anytime predictors not only generate approximate predictions rapidly, but are capable of using extra resources at prediction time, when available, to improve performance. We also demonstrate how our framework can be used to select weak predictors which target certain subsets of the data, allowing for efficient use of computational resources on difficult examples. We also show that variants of the SpeedBoost algorithm produce predictors which are provably competitive with any possible sequence of weak predictors with the same total complexity. 1
