7,825 research outputs found
Collaborative design : managing task interdependencies and multiple perspectives
This paper focuses on two characteristics of collaborative design with
respect to cooperative work: the importance of work interdependencies linked to
the nature of design problems; and the fundamental function of design
cooperative work arrangement which is the confrontation and combination of
perspectives. These two intrinsic characteristics of the design work stress
specific cooperative processes: coordination processes in order to manage task
interdependencies, establishment of common ground and negotiation mechanisms in
order to manage the integration of multiple perspectives in design
Designing difficult office space allocation problem instances with mathematical programming
Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters
Asynchronous Decentralized Task Allocation for Dynamic Environments
This work builds on a decentralized task allocation algorithm for networked agents communicating through an asynchronous channel, by extending the Asynchronous Consensus-Based Bundle Algorithm (ACBBA) to account for more real time implementation issues resulting from a decentralized planner. This paper specfically talks to the comparisons between global and local convergence in asynchronous consensus algorithms. Also a feature called asynchronous replan is introduced to ACBBA's functionality that enables e ffcient updates to large changes in local situational awareness. A real-time software implementation using multiple agents communicating through the user datagram protocol (UDP) validates the proposed algorithm.United States. Air Force (grant FA9550-08-1-0086)United States. Air Force Office of Scientific Research (grant FA9550-08-1-0086)Aurora Flight Sciences Corp. (SBIR - FA8750-10-C-0107
Designing difficult office space allocation problem instances with mathematical programming
Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters
A 0/1 integer programming model for the office space allocation problem
We propose a 0/1 integer programming model to tackle the office space allocation (OSA) problem which refers to assigning room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional requirements. In the proposed approach, these requirements can be modelled as constraints (hard constraints) or as objectives (soft constraints). Then, we conduct some experiments on benchmark instances and observe that setting certain constraints as hard (actual constraints) or soft (objectives) has a significant impact on the computational difficulty on this combinatorial optimisation problem
Downlink Noncoherent Cooperation without Transmitter Phase Alignment
Multicell joint processing can mitigate inter-cell interference and thereby
increase the spectral efficiency of cellular systems. Most previous work has
assumed phase-aligned (coherent) transmissions from different base transceiver
stations (BTSs), which is difficult to achieve in practice. In this work, a
noncoherent cooperative transmission scheme for the downlink is studied, which
does not require phase alignment. The focus is on jointly serving two users in
adjacent cells sharing the same resource block. The two BTSs partially share
their messages through a backhaul link, and each BTS transmits a superposition
of two codewords, one for each receiver. Each receiver decodes its own message,
and treats the signals for the other receiver as background noise. With
narrowband transmissions the achievable rate region and maximum achievable
weighted sum rate are characterized by optimizing the power allocation (and the
beamforming vectors in the case of multiple transmit antennas) at each BTS
between its two codewords. For a wideband (multicarrier) system, a dual
formulation of the optimal power allocation problem across sub-carriers is
presented, which can be efficiently solved by numerical methods. Results show
that the proposed cooperation scheme can improve the sum rate substantially in
the low to moderate signal-to-noise ratio (SNR) range.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Research on fully distributed data processing systems
Issued as Quarterly progress reports, nos. 1-11, and Project report, Project no. G-36-64
Collaborative Decision-Making and the k-Strong Price of Anarchy in Common Interest Games
The control of large-scale, multi-agent systems often entails distributing
decision-making across the system components. However, with advances in
communication and computation technologies, we can consider new collaborative
decision-making paradigms that exist somewhere between centralized and
distributed control. In this work, we seek to understand the benefits and costs
of increased collaborative communication in multi-agent systems. We
specifically study this in the context of common interest games in which groups
of up to k agents can coordinate their actions in maximizing the common
objective function. The equilibria that emerge in these systems are the
k-strong Nash equilibria of the common interest game; studying the properties
of these states can provide relevant insights into the efficacy of inter-agent
collaboration. Our contributions come threefold: 1) provide bounds on how well
k-strong Nash equilibria approximate the optimal system welfare, formalized by
the k-strong price of anarchy, 2) study the run-time and transient performance
of collaborative agent-based dynamics, and 3) consider the task of redesigning
objectives for groups of agents which improve system performance. We study
these three facets generally as well as in the context of resource allocation
problems, in which we provide tractable linear programs that give tight bounds
on the k-strong price of anarchy.Comment: arXiv admin note: text overlap with arXiv:2308.0804
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