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273
Distributed intelligence for multi-camera visual surveillance
- PATTERN RECOGNITION
, 2003
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Team-Oriented Agent Coordination in the RETSINA Multi-Agent System
- Robotics Institute, Carnegie Mellon University
, 2002
"... individual has the collective expertise, information, or resources required for the eective completion or performance of a task. This paper describes a prototype, implemented in the RETSINA multi-agent infrastructure, in which agents interact with each other via capability-based and team-oriented co ..."
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Cited by 32 (4 self)
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individual has the collective expertise, information, or resources required for the eective completion or performance of a task. This paper describes a prototype, implemented in the RETSINA multi-agent infrastructure, in which agents interact with each other via capability-based and team-oriented coordination. We propose a model of team-oriented agent coordination that is based on the joint intentions theory, so that agents can communicate their intended commitments to each other. Team-oriented agents communicate partial descriptions of the context in which a mission must be executed and the resources to do so via data structures that are analogous to the SharedPlans recipe. The agents then proceed, in a process reminiscent of SharedPlans partial plan re- nement, to re ne and revise their understanding of the mission context, via both team-oriented and capability-based coordination with other RETSINA agents, while executing their mission. The partial plan re nement behavior is made possible through the RETSINA Agent Architecture, which interleaves HTN planning and process execution. We enhance the above models of teamwork by adding our own characterizations of checkpoints, role and subgoal relations in software agent teamwork, and show how the software agents can acquire this information from their operating environment during plan execution time. Such enhancements create a scalable team-oriented multi-agent system architecture, in which team coordination strategies can be implemented in a general and domain-independent way.
A survey of collectives
- IN COLLECTIVES AND THE DESIGN OF COMPLEX SYSTEMS
, 2004
"... Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a welldefined set of system-level performance cr ..."
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Cited by 28 (12 self)
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Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a welldefined set of system-level performance criteria, are called collectives. The fundamental problem in analyzing/designing such systems is in determining how the combined actions of a large number of agents leads to “coordinated ” behavior on the global scale. Examples of artificial systems which exhibit such behavior include packet routing across a data network, control of an array of communication satellites, coordination of multiple rovers, and dynamic job scheduling across a distributed computer grid. Examples of natural systems include ecosystems, economies, and the organelles within a living cell. No current scientific discipline provides a thorough understanding of the relation between the structure of collectives and how well they meet their overall performance criteria. Although still very young, research on collectives has resulted in successes both in understanding and designing such systems. It is expected that as it matures and draws upon other disciplines related to collectives, this field will greatly expand the range of computationally addressable tasks. Moreover, in addition to drawing on them, such a fully developed field of collective intelligence may provide insight into already established scientific fields, such as mechanism design, economics, game theory, and population biology. This chapter provides a survey to the emerging science of collectives.
A capabilities-based model for adaptive organizations
- AAMAS
"... Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environm ..."
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Cited by 28 (8 self)
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Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.
Cognitive architectures and general intelligent systems
- AI Magazine
, 2006
"... The original goal of artificial intelligence was the design and construction of computational artifacts that combined many cognitive abilities in an integrated system. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence i ..."
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Cited by 26 (2 self)
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The original goal of artificial intelligence was the design and construction of computational artifacts that combined many cognitive abilities in an integrated system. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence in a general way across many different domains. We will refer to this research agenda as aimed at the creation of general intelligent systems. Unfortunately, modern artificial intelligence has largely abandoned this objective, having instead divided into many distinct subfields that care little about generality, intelligence, or even systems. Subfields like computational linguistics, planning, and computer vision focus their attention on specific components that underlie intelligent behavior, but seldom show concern about how they might interact with each other. Subfields like knowledge representation and machine learning focus on idealized tasks like inheritance, classification, and reactive control that ignore the richness and complexity of human intelligence. The fragmentation of artificial intelligence has taken energy away from efforts on general intelligent systems, but it has led to certain types of progress within each of its subfields. Despite this subdivision into distinct communities, the past decade has seen many applications of AI technology
A Survey on Sensor Networks from a Multi-Agent perspective
"... Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of real-world applications. Mult ..."
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Cited by 26 (0 self)
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Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of real-world applications. Multi-Agent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous self-aware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research. 1.
Modeling and Simulating Human Teamwork Behaviors Using Intelligent Agents
- In Journal of Physics of Life Reviews
, 2004
"... Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively ..."
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Cited by 21 (1 self)
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Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human/agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork--- shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.
Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs
, 2013
"... This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A * (GMAA*) algorithm, which ..."
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Cited by 18 (12 self)
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This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A * (GMAA*) algorithm, which reduces the problem to a tree of one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs that can be solved optimally. First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality. Second, we introduce incremental expansion of nodes in the GMAA * search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the node’s depth. This is particularly beneficial when little clustering is possible. In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs. We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent. Finally, we present extensive empirical results demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can optimally solve Dec-POMDPs of unprecedented size.
Collective intelligence, data routing and Braess’ paradox
- JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2002
"... We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system (MAS) so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the router ..."
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Cited by 18 (12 self)
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We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system (MAS) so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent’s actions on another agent’s performance, having agents use ISPA’s is suboptimal as far as global aggregate cost is concerned, even when they are only used to route in£nitesimally small amounts of traf£c. The utility functions of the individual agents are not “aligned” with the global utility, intuitively speaking. As a particular example of this we present an instance of Braess’ paradox in which adding new links to a network whose agents all use the ISPA results in a decrease in overall throughput. We also demonstrate that load-balancing, in which the agents ’ decisions are collectively made to optimize the global cost incurred by all traf£c currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to “side-effects”, in this case of current routing decision on future traf£c. The mathematics of Collective
Using Decision Theory to Formalize Emotions for Multi-Agent System Applications: Preliminary Report
- Proc. of the Second Workshop on Decision Theoretic and Game Theoretic Agents, held in conjunction with the Fourth International Conference on Multi-Agent Systems (ICMAS'2000
, 2000
"... We use the formalism of decision theory to develop principled definitions of emotional states of a rational agent. We postulate that these notions are useful for rational agent design. First, they can serve as internal states controlling the allocation of computations and time devoted to cognitiv ..."
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Cited by 18 (1 self)
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We use the formalism of decision theory to develop principled definitions of emotional states of a rational agent. We postulate that these notions are useful for rational agent design. First, they can serve as internal states controlling the allocation of computations and time devoted to cognitive tasks under external pressures. Second, they provide a well defined implementation-independent vocabulary the agents can use to communicate their internal states to each other. Finally, they are essential during interactions with human agents in open multi-agent environments. Using decision theory to formalize the notions of emotions provides a formal bridge between the rich bodies of work in cognitive science, on the one hand, and the high-end AI architectures for designing rational artificial agents, on the other hand. 1 Introduction Our research is predicated on the thesis that concept of emotions and feelings can be formalized and be made useful in designing artificial agents t...
