We examine the effects of inter-agent variation on the ability of a decentralized multi-agent system (MAS) to self-organize in response to dynamically changing task demands. In decentralized biological systems, inter-agent variation as minor as noise has been observed to improve a system's ability to redistribute agent resources in response to external stimuli. We compare the performance of two MAS consisting of agents with and without noisy sensors on a cooperative tracking problem and examine the effects of inter-agent variation on agent behaviors and how those behaviors affect system performance. Results show that small variations in how individual agents respond to stimuli can lead to more accurate and stable allocation of agent resources.
Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy (i.e., decide when and where to collect sensing data) to achieve an optimized tradeoff between the data quality and the sensing cost. While recent progress has been made to tackle similar problems, three important challenges have not been well addressed: (i) "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables in social sensing; (ii) "multi-attribute constrained optimization": minimizing the overall sensing error given the dependencies and constraints of multiple attributes of the measured variables is a nontrivial problem to solve; (iii) "nonuniform task allocation cost": the task allocation cost in social sensing often has a nonuniform distribution which adds additional complexity to the optimized task allocation problem. We evaluate the QCO-TA scheme through a real-world social sensing application and the results show that our scheme significantly outperforms the state-of-the-art baselines in terms of both sensing accuracy and cost. Introduction This paper presents an online reinforcement learning framework to solve the quality-cost-aware task allocation problem in multi-attribute social sensing applications. Social sensing has emerged as a new sensing paradigm in pervasive and mobile computing applications where humans (or devices on their behalf) collectively report measurements about the physical world [1, 2]. Examples of social sensing applications include air quality and environment monitoring in smart cities using mobile devices , malfunctioning urban infrastructures reporting using geotagging , and damage assessment in disaster response using online social media . In social sensing applications, participants perform sensing tasks at assigned locations to collect different attributes of the measured variables that are of interests to the application . For example, in an urban air quality sensing application, participants are tasked to measure various air quality attributes (e.g., PM 2.5, PM 10, CO 2) at different locations of the city to estimate the overall air quality and identity potential health risks. We refer to this category of applications as multi-attribute social sensing applications . In multi-attribute social sensing applications, there exists a fundamental tradeoff between data quality and sensing (task allocation) cost [3, 7]. 2 In particular, it is essential to obtain comprehensive and accurate measurements to ensure the desired data quality of the social sensing applications.
AMC (Automatic Modulation and Coding) scheme used in LTE (Long Term Evolution) networks can select an adequate modulation technique, such as BPSK, QPSK, and QAM, based on the channel quality of a UE (User Equipment) . From the requested data rate and the selected modulation technique, the number of RB (Resource Blocks) required for a UE can be determined. Thus, in this paper, by estimating the processing capability of a UE and by adapting to the changeable channel quality, we propose a dynamic allocation scheme for RB by utilizing the status report of automatic repeated request (ARQ) . Previous work on RB allocations in LTE networks can be divided into three categories. The first category allocates RB based on the requested Data Rate (DR) of UE.
The problem of multi-agent task allocation arises in a variety of scenarios involving human teams. In many such settings, human teammates may act with selfish motives and try to minimize their cost metrics. In the absence of (1) complete knowledge about the reward of other agents and (2) the team's overall cost associated with a particular allocation outcome, distributed algorithms can only arrive at sub-optimal solutions within a reasonable amount of time. To address these challenges, we introduce the notion of an AI Task Allocator (AITA) that, with complete knowledge, comes up with fair allocations that strike a balance between the individual human costs and the team's performance cost. To ensure that AITA is explicable to the humans, we allow each human agent to question AITA's proposed allocation with counterfactual allocations. In response, we design AITA to provide a replay negotiation tree that acts as an explanation showing why the counterfactual allocation, with the correct costs, will eventually result in a sub-optimal allocation. This explanation also updates a human's incomplete knowledge about their teammate's and the team's actual costs. We then investigate whether humans are (1) able to understand the explanations provided and (2) convinced by it using human factor studies. Finally, we show the effect of various kinds of incompleteness on the length of explanations. We conclude that underestimation of other's costs often leads to the need for explanations and in turn, longer explanations on average.
We consider the problem of fairly allocating a set of indivisible goods among n agents. Various fairness notions have been proposed within the rapidly growing field of fair division, but the Nash social welfare (NSW) serves as a focal point. In part, this follows from the ‘unreasonable’ fairness guarantees provided, in the sense that a max NSW allocation meets multiple other fairness metrics simultaneously, all while satisfying a standard economic concept of efficiency, Pareto optimality. However, existing approximation algorithms fail to satisfy all of the remarkable fairness guarantees offered by a max NSW allocation, instead targeting only the specific NSW objective. We address this issue by presenting a 2 max NSW, Prop-1, 1/(2n) MMS, and Pareto optimal allocation in strongly polynomial time. Our techniques are based on a market interpretation of a fractional max NSW allocation. We present novel definitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation.