scholarly journals Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty

2009 ◽  
Vol 35 ◽  
pp. 119-159 ◽  
Author(s):  
S. D. Ramchurn ◽  
C. Mezzetti ◽  
A. Giovannucci ◽  
J. A. Rodriguez-Aguilar ◽  
R. K. Dash ◽  
...  
Keyword(s):  
Task Allocation ◽  
Social Utility ◽  
Rational Agents ◽  
New Class ◽  
Incentive Compatible ◽  
Subjective Perceptions ◽  
Opt Out ◽  
Real World Applications ◽  
Np Complete ◽  
Programming Solution

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will "always" successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of "trust". Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called "trust-based mechanisms", that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^5 possible allocations in 40 seconds).

scholarly journals Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2117
Author(s):  
Ming Chong Lim ◽  
Han-Lim Choi
Keyword(s):  
Task Allocation ◽  
Random Sampling ◽  
Scheduling Algorithm ◽  
Stochastic Algorithm ◽  
Speed Of Convergence ◽  
Temporal Dependence ◽  
Real World Applications ◽  
Coupled Constraints ◽  
Multi Agent ◽  
Feasible Solutions

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

Priority-based task scheduling on heterogeneous resources in the Expert Cloud

Kybernetes ◽  
2015 ◽  
Vol 44 (10) ◽  
pp. 1455-1471 ◽  
Author(s):  
Mehran Ashouraie ◽  
Nima Jafari Navimipour
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Design Methodology ◽  
Fitness Function ◽  
Content Type ◽  
Cloud Systems ◽  
New Class ◽  
Complete Problems ◽  
Np Complete ◽  
Real World Situation

Purpose – Expert Cloud as a new class of Cloud systems provides the knowledge and skills of human resources (HRs) as a service using Cloud concepts. Task scheduling in the Expert Cloud is a vital part that assigns tasks to suitable resources for execution. The purpose of this paper is to propose a method based on genetic algorithm to consider the priority of arriving tasks and the heterogeneity of HRs. Also, to simulate a real world situation, the authors consider the human-based features of resources like trust, reputation and etc. Design/methodology/approach – As it is NP-Complete to schedule tasks to obtain the minimum makespan and the success of genetic algorithm in optimization and NP-Complete problems, the authors used a genetic algorithm to schedule the tasks on HRs in the Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on several factors; one point cross-over and swap mutation are also used. Findings – The obtained results demonstrated the efficiency of the proposed algorithm in terms of time complexity, task fail rate and HRs utilization. Originality/value – In this paper the task scheduling issue in the Expert Cloud and improving pervious algorithm are pointed out and the approach to resolve the problem is applied into a practical example.

GRAPH BISECTION MODELED AS CARDINALITY CONSTRAINED BINARY QUADRATIC TASK ALLOCATION

2013 ◽  
Vol 12 (02) ◽  
pp. 261-276 ◽  
Author(s):  
MARK LEWIS ◽  
GARY KOCHENBERGER
Keyword(s):  
Task Allocation ◽  
Graph Partitioning ◽  
State Of The Art ◽  
Quadratic Model ◽  
Complete Problem ◽  
Binary Model ◽  
Comparison Results ◽  
Graph Bisection ◽  
Strategic Oscillation ◽  
Np Complete

In this paper, the cardinality constrained quadratic model for binary quadratic programming is used to model and solve the graph bisection problem as well as its generalization in the form of the task allocation problem with two processors (2-TAP). Balanced graph bisection is an NP-complete problem which partitions a set of nodes in the graph G = (N, E) into two sets with equal cardinality such that a minimal sum of edge weights exists between the nodes in the two separate sets. 2-TAP is graph bisection with the addition of node preference costs in the objective function. We transform the general linear k-TAP model to the cardinality constrained quadratic binary model so that it may be efficiently solved using tabu search with strategic oscillation. On a set of benchmark graph bisections, we improve the best known solution for several problems. Comparison results with the state-of-the-art graph partitioning program METIS, as well as Cplex and Gurobi are presented on a set of randomly generated graphs. This approach is shown to also work well with 2-TAP, comparing favorably to Cplex and Gurobi, providing better solutions in a much shorter time.

scholarly journals Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

2017 ◽  
Vol 112 ◽  
pp. 91-98 ◽  
Author(s):  
Naoki Iijima ◽  
Ayumi Sugiyama ◽  
Masashi Hayano ◽  
Toshiharu Sugawara
Keyword(s):  
Task Allocation ◽  
Individual Preference ◽  
Social Utility ◽  
Distributed Environments

scholarly journals Rationality in games and institutions

Synthese ◽  
2021 ◽  
Author(s):  
Philippe van Basshuysen
Keyword(s):  
Institutional Design ◽  
Incentive Compatibility ◽  
Social Goals ◽  
General Constraint ◽  
Rational Agents ◽  
Economic Agents ◽  
Incentive Compatible ◽  
Inclusive Concept ◽  
The One ◽  
Orthodox View

AbstractAgainst the orthodox view of the Nash equilibrium as “the embodiment of the idea that economic agents are rational” (Aumann, 1985, p 43), some theorists have proposed ‘non-classical’ concepts of rationality in games, arguing that rational agents should be capable of improving upon inefficient equilibrium outcomes. This paper considers some implications of these proposals for economic theory, by focusing on institutional design. I argue that revisionist concepts of rationality conflict with the constraint that institutions should be designed to be incentive-compatible, that is, that they should implement social goals in equilibrium. To resolve this conflict, proponents of revisionist concepts face a choice between three options: (1) reject incentive compatibility as a general constraint, (2) deny that individuals interacting through the designed institutions are rational, or (3) accept that their concepts do not cover institutional design. I critically discuss these options and I argue that a more inclusive concept of rationality, e.g. the one provided by Robert Sugden’s version of team reasoning, holds the most promise for the non-classical project, yielding a novel argument for incentive compatibility as a general constraint.

Review on Multiobjective Task Scheduling in Cloud Computing using Nature Inspired Algorithms

2018 ◽  
Vol 6 (8) ◽  
pp. 282
Author(s):  
Shikha Chaudhary ◽  
Saroj Hiranwal ◽  
C. P. Gupta
Keyword(s):  
Cloud Computing ◽  
Optimal Solution ◽  
Computing System ◽  
Ant Colony Optimization Algorithm ◽  
New Class ◽  
Complete Problem ◽  
Nature Inspired Algorithm ◽  
Cloud Computing System ◽  
Np Complete ◽  
Nature Inspired Algorithms

In cloud computing huge pool of resources are available and shared through internet. The scheduling is a core technique which determines the performance of a cloud computing system. The goal of scheduling is to allocate task to appropriate machine to achieve one or more QOS. To find the suitable resource among pool of resources to achieve the goal is an NP Complete problem. A new class of algorithm called nature inspired algorithm came into existence to find optimal solution.  In this paper we provide a survey as well as a comparative analysis of various existing nature inspired scheduling algorithms which are based on genetic algorithm and ant colony optimization algorithm. 

scholarly journals A Novel Task Allocation Algorithm in Mobile Crowdsensing with Spatial Privacy Preservation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Wenyi Tang ◽  
Qi Jin ◽  
Xu Zheng ◽  
Guangchun Luo ◽  
Guiduo Duan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Task Allocation ◽  
Privacy Preservation ◽  
The Internet ◽  
Fundamental Issue ◽  
Sufficient Data ◽  
Mobile Crowdsensing ◽  
Real World Applications ◽  
Step Algorithm ◽  
The Internet Of Things

The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.

ON THE INTRACTABILITY OF TASK ALLOCATION IN DISTRIBUTED SYSTEMS

1994 ◽  
Vol 04 (01n02) ◽  
pp. 149-157 ◽  
Author(s):  
HESHAM H. ALI ◽  
HESHAM EL-REWINI
Keyword(s):  
Distributed Systems ◽  
Distributed Computing ◽  
Task Allocation ◽  
Formal Proof ◽  
Computer Applications ◽  
Allocation Problem ◽  
Distributed Computing Systems ◽  
Clique Partitioning ◽  
Split Graphs ◽  
Np Complete

The fast progress of large integration technology has made distributed computing economically attractive for many computer applications. Task allocation is one of the most important and challenging problems in distributed computing systems that has received considerable attention in recent years. Several researchers have introduced different heuristics to solve this problem with the assumption that it is computationally intractable. These researchers have been referring to an early work by Stone and some private communication as their sources of the problem’s intractability. However, neither Stone’s paper nor any other related work in the literature provided a formal proof of intractability. Due to the importance of the task allocation problem, we believe that a formal proof of its complexity must be provided. In this paper we provide a proof that the problem of task allocation is intractable even in the restricted case when there are only two values of the the communication cost: zero or one. We introduce a new model to represent the problem of allocating tasks on heterogeneous distributed systems using split graphs. This model allows us to relate the task allocation problem with the problem of weighted clique partitioning in complete split graphs. We provide a two-step reduction method to prove the intractability of task allocation in the restricted case mentioned above. In the first step, we prove that the problem of weighted clique partitioning in complete split graph is NP-complete using a transformation from the problem of partition into triangles. In the second step, we show that the task allocation problem is NP-complete using a transformation from the weighted clique partitioning problem.

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