Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads and extinguish the fires which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximize the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralized fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long-range communication devices. Against this background, we provide a novel decentralized solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a coalition formation with spatial and temporal constraints (CFST) problem where agents form coalitions to complete tasks, each with different demands. To design a decentralized algorithm for CFST, we formulate it as a distributed constraint optimization problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralized message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralized algorithms used for this problem.

In open multi-agent systems, linked data enables agents to communicate with each other and to gather knowledge for autonomous decision. Until now, trust is a factor for starting communications and ignores doubts about the content or context of ongoing communications. Several approaches are used to identify whom to trust and how human trust can be computationally modeled. Yet, they do not consider a change of context or of other agents’ behavior at runtime. The proposed doctoral work aims to support content- and context-related trust in open multi-agent systems using linked data. Existing trust models need to be surveyed with respect to content- and context-related trust. A framework based on a fitting trust model and working with linked data must be developed to establish and dynamically refine trust relationships on the autonomous agents’ point of view. This would enhance the applicability of decentralized systems without introducing central units as the history of the web demonstrates. Web engineers are hereby supported to work on a new level of abstraction using the decentralization, but not scrutinizing specific communication sequences.

Agents frequently collaborate to achieve a shared goal or to accomplish a task that they cannot do alone. However, collaboration is difficult in open multi-agent systems where agents share constrained resources to achieve both individual and shared goals. In current approaches to collaboration, agents are organised into disjoint groups and social reasoning is used to capture their capabilities when selecting a qualified set of collaborators. These approaches are not useful when agents are in multiple, overlapping groups; depend on each other when using shared resources; have multiple goals to achieve simultaneously; and have to share the overall costs and benefits. In this article, agents use social reasoning to enhance their understanding of other agents’ goals and their dependencies, and self-adaptive techniques to adapt their level of self-interest in a collaborative process, with a view to contributing to lowering shared costs or increasing shared benefits. This model aims at improving the extent to which agents’ goals are met while improving shared resource usage efficiency. For example, in a public transport system where each mode of transport has limited capacity, commuters will be enabled to make choices that avoid over-capacity in different modes, or in a smart energy grid with limited capacity, users can make choices as to when they increase their demand. The model simultaneously helps avoid overloading a shared resource while allowing users to achieve their own goals. The proposed model is evaluated in an open multi-agent system with 100 agents operating in multiple overlapping groups and sharing multiple constrained resources. The impact of agents’ varying levels of social dependencies, mobility, and their groups’ density on their individual and shared goal achievement is analysed.

The understanding of biological systems relies on the accurate description of the interaction among biomolecules. This knowledge can be obtained by in vitro assays involving interacting partners with well-defined compositional, spatial, and temporal constraints. The distinguishing features of living systems, namely, low copy number, crowded environment, and spatial compartmentalization, are usually absent in most in vitro experiments reported in the literature. This thesis discusses the implications of low copy number and spatial constraints using theoretical and computational methods in some model systems. Furthermore, two experimental platforms, based on the recent development of microfluidic techniques, are described in detail. In the first implementation, micronsized chambers fabricated using soft lithography provide a high-throughput reactor array whose size and composition can be configured to mimic the in vivo environment. The second design reports the generation and manipulation of femtoliter-volume water-in-oil droplets. A model biochemical reaction catalyzed by beta-galactosidase is observed in both reactors with precisely defined initiation time, opening the way to monitor transient kinetics in addition to steady-state behavior. Additionally, the enzymatic activity exhibits a negative correlation with the size of water-in-oil droplets when the nominal concentrations of reagents are kept the same. This surprising result is analyzed in detail by carefully designed control experiments, and attributed to the shear-induced redistribution of surfactant employed to stabilize the water/oil interface. Specifically, smaller droplets experience bigger shear stress, which change the surface concentration of surfactant and allow for the nonspecific binding of proteins to the interface. Surface-bound enzymes are denatured, leading to reduced catalytic activity. This highly dynamic process is hardly detectable by other methods such as tensiometry or direct fluorescence imaging of the interface.

The paper presents an algorithm attempting to combine both intra-frame and interframe information to reconstruct lost macroblocks due to imperfect communication channels when decoding a MPEG bitstream. The algorithm is a POCS-based (Projection Onto the Convex Set) iterative restoration algorithm incorporating both the temporal and spatial constraints derived from a set of analysis performed on the picture sequence. Often the use of the temporal information in the restoration process is complicated by the scene changes or large random motion activities. To reliably utilize the temporal information, we formulate a series of test to determine the usefulness of the temporal information. In addition, the tests yield a temporal constraint if the temporal information is deemed good. Along with the spatial constraints as described in [1], the temporal constraint is used in the proposed iterative restoration algorithm.

Software agents’ ability to interact within different open systems, designed by different groups, presupposes an agreement on an unambiguous definition of a set of concepts, used to describe the context of the interaction and the communication language the agents can use. Agents’ interactions ought to allow for reliable expectations on the possible evolution of the system; however, in open systems interacting agents may not conform to predefined specifications. A possible solution is to define interaction environments including a normative component, with suitable rules to regulate the behaviour of agents. To tackle this problem we propose an application-independent metamodel of artificial institutions that can be used to define open multiagent systems. In our view an artificial institution is made up by an ontology that models the social context of the interaction, a set of authorizations to act on the institutional context, a set of linguistic conventions for the performance of institutional actions and a system of norms that are necessary to constrain the agents’ actions.

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper.

Single-rate data flow graphs (DFGs) are often used for modeling iterative concurrent activities. A DFG and its iteration bound are equivalent to a marked graph and cycle time of a Petri net, respectively. Ramamoorthy and Ho (1980) developed an efficient algorithm for checking the minimum cycle time against a predetermined performance requirement. This work presents a systematic procedure to find the iteration bound and the critical loop with time complexity O(n/sup 3/ log n) (n being the number of nodes), memory requirements of O(n/sup 2/), and subcritical loops with time complexity O(n/sup 3/). The next-critical loops are also studied because they may become the new critical loop if the look-ahead technique is used. The above procedure has been implemented in the C programming language which interfaces with a Petri net X-window tool to display the performance results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Open Multiagent Systems, in which heterogeneous agents interact with each other and organize themselves into Virtual Organizations, demand infrastructures supporting these features. In these systems, dynamic and complex interactions between agents may arise. Interaction Protocols allow the definition of communication patterns. However in open systems, dynamic and complex interactions may also require these patterns be modified at execution time. We propose a support for modelling complex, concurrent and dynamic interactions between agents in terms of conversations. Conversations between agents follow predefined Interaction Protocols that can be dynamically modified without restarting the system. This support is provided at agent level and is integrated into the Magentix Multiagent Platform.

We present an algorithm for estimating a sequence of articulated upper-body human pose in unconstrained videos. Most previous work often fails to locate forearms in those video scenes suffering from illumination varieties, background clutter, camera shake, or occlusion. In order to deal with such intractable cases, we propose a novel algorithm for addressing the problem of certain body parts localization. The proposed approach can be roughly divided into two steps: first, a spatial model is designed to capture the high-order relationship between adjacent parts and meanwhile to generate a set of configurations in each frame under the temporal context constraint; second, a competitive method is presented to select the best body parts among diverse pose configurations. In this paper, the proposed algorithm focuses on the unconstrained video scenes and improves the detection precision of certain body parts with high degree of freedom. Moreover, the proposed algorithm can be well applied to a very challenging dataset named Movies. Experimental results show that the proposed algorithm can dramatically improve performance compared with those related algorithms on two benchmark datasets (MPII and SHPED datasets) and on our Movies dataset.

In open multi-agent systems trust is necessary to improve cooperation by enabling agents to choose good partners. Most trust models work by taking, in addition to direct experiences, other agents' communicated evaluations into account. However, in an open multi-agent system other agents may use different trust models and as such the evaluations they communicate are based on different principles. This article shows that trust alignment is a crucial tool in this communication. Furthermore we show that trust alignment improves significantly if the description of the evidence, upon which a trust evaluation is based, is taken into account.

Motivated by Boeing 777 fully autonomous upright build project, orchestration of human and robotic agents are studied. Tasks must be precisely allocated, sequenced, and coordinated among agents subject to temporal and spatial constraints. The problem is formulated as a flexible job shop with sequence-dependent setup to capture heterogeneous agents and travel time. Two exact central approaches are proposed: a mixed integer programming and a constraint programming, and tested for real-time perspective. The computational study demonstrates the proposed method can generate optimal schedules up to 100 agents with 1000 subtasks that requires 10 subtasks per agent on average, within 183 seconds, a substantial improvement over all other benchmark approaches in the literature.

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Open systems are complex, heterogeneous systems whose complexity is often handled by component-based approaches. While the internal functioning of such components is invisible to the outside, a great emphasis is put on the verification of properties that ensure a safe, predictable, and understandable external behaviour. At the same time, the rules that govern and describe the system behaviour must guarantee maximum flexibility. Developing technologies that can satisfy the requirements of open multi-agent systems poses an important and difficult challenge. Here, declarative approaches have the potential of offering solutions satisfying the needs for both specifying and developing open multi-agent systems and other open systems for diverse applications such as business processes, clinical guidelines and cloud computing.KeywordsCloud ComputingBusiness ProcessMultiagent SystemInternal FunctioningBusiness Process ManagementThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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