scholarly journals Eliciting Single-Peaked Preferences Using Comparison Queries

2009 ◽  
Vol 35 ◽  
pp. 161-191 ◽  
Author(s):  
V. Conitzer
Keyword(s):  
Preference Elicitation ◽  
Experimental Results ◽  
Prior Work ◽  
Multiple Agents ◽  
Logarithmic Number ◽  
General Method

Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to simply ask agents to report their complete preferences. Rather, the agents' preferences, or at least the relevant parts thereof, need to be elicited. This is done by asking the agents a (hopefully small) number of simple queries about their preferences, such as comparison queries, which ask an agent to compare two of the alternatives. Prior work on preference elicitation in voting has focused on the case of unrestricted preferences. It has been shown that in this setting, it is sometimes necessary to ask each agent (almost) as many queries as would be required to determine an arbitrary ranking of the alternatives. In contrast, in this paper, we focus on single-peaked preferences. We show that such preferences can be elicited using only a linear number of comparison queries, if either the order with respect to which preferences are single-peaked is known, or at least one other agent's complete preferences are known. We show that using a sublinear number of queries does not suffice. We also consider the case of cardinally single-peaked preferences. For this case, we show that if the alternatives' cardinal positions are known, then an agent's preferences can be elicited using only a logarithmic number of queries; however, we also show that if the cardinal positions are not known, then a sublinear number of queries does not suffice. We present experimental results for all elicitation algorithms. We also consider the problem of only eliciting enough information to determine the aggregate ranking, and show that even for this more modest objective, a sublinear number of queries per agent does not suffice for known ordinal or unknown cardinal positions. Finally, we discuss whether and how these techniques can be applied when preferences are almost single-peaked.

scholarly journals Towards High-Level Intrinsic Exploration in Reinforcement Learning

2020 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
State Of The Art ◽  
Experimental Results ◽  
Prior Work ◽  
Extrinsic Rewards ◽  
Intrinsic Reward ◽  
Long Time ◽  
End To End ◽  
High Level

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

scholarly journals LUT Based Generalized Parallel Counters for State - of - art FPGAs

2017 ◽  
Vol 21 (1) ◽  
pp. 3
Author(s):  
Burhan Khurshid
Keyword(s):  
Power Dissipation ◽  
High Speed ◽  
Critical Path ◽  
Experimental Results ◽  
Considerable Reduction ◽  
Prior Work ◽  
Dynamic Power ◽  
Look Up Table ◽  
State Of Art

Generalized Parallel Counters (GPCs) are frequently used in constructing high speed compressor trees. Previous work has focused on achieving efficient mapping of GPCs on FPGAs by using a combination of general Look-up table (LUT) fabric and specialized fast carry chains. The  resulting structures are purely combinational and cannot be efficiently pipelined to achieve the potential FPGA performance. In this paper, we take an alternate approach and try to eliminate the fast carry chain from the GPC structure. We present a heuristic that maps GPCs on FPGAS using only general LUT fabric. The resultant GPCs are then easily re-timed by placing registers at the fan-out nodes of each LUT. We have used our heuristic on various GPCs reported in prior work. Our heuristic successfully eliminates the carry chain from the GPC structure with the same LUT count in most of the cases. Experimental results using Xilinx Kintex-7 FPGAs show a considerable reduction in critical path and dynamic power dissipation with same area utilization in most of the cases.

scholarly journals Multiple weak supervision for short text classification

2022 ◽  
Author(s):  
Li-Ming Chen ◽  
Bao-Xin Xiu ◽  
Zhao-Yun Ding
Keyword(s):  
Text Classification ◽  
Classification Problem ◽  
Experimental Results ◽  
Prior Work ◽  
Weak Supervision ◽  
Short Text ◽  
Imbalanced Classification ◽  
Distant Supervision ◽  
Synthetic Datasets ◽  
Independent Model

AbstractFor short text classification, insufficient labeled data, data sparsity, and imbalanced classification have become three major challenges. For this, we proposed multiple weak supervision, which can label unlabeled data automatically. Different from prior work, the proposed method can generate probabilistic labels through conditional independent model. What’s more, experiments were conducted to verify the effectiveness of multiple weak supervision. According to experimental results on public dadasets, real datasets and synthetic datasets, unlabeled imbalanced short text classification problem can be solved effectively by multiple weak supervision. Notably, without reducing precision, recall, and F1-score can be improved by adding distant supervision clustering, which can be used to meet different application needs.

scholarly journals PROPm Allocations of Indivisible Goods to Multiple Agents

2021 ◽  
Author(s):  
Artem Baklanov ◽  
Pranav Garimidi ◽  
Vasilis Gkatzelis ◽  
Daniel Schoepflin
Keyword(s):  
Prior Work ◽  
Indivisible Goods ◽  
Multiple Agents ◽  
Running Time ◽  
Classic Problem

We study the classic problem of fairly allocating a set of indivisible goods among a group of agents, and focus on the notion of approximate proportionality known as PROPm. Prior work showed that there exists an allocation that satisfies this notion of fairness for instances involving up to five agents, but fell short of proving that this is true in general. We extend this result to show that a PROPm allocation is guaranteed to exist for all instances, independent of the number of agents or goods. Our proof is constructive, providing an algorithm that computes such an allocation and, unlike prior work, the running time of this algorithm is polynomial in both the number of agents and the number of goods.

scholarly journals Multi-Agent Pathfinding with Continuous Time

2019 ◽  
Author(s):  
Anton Andreychuk ◽  
Konstantin Yakovlev ◽  
Dor Atzmon ◽  
Roni Stern
Keyword(s):  
Path Planning ◽  
Continuous Time ◽  
Single Agent ◽  
Optimal Solutions ◽  
Prior Work ◽  
Multiple Agents ◽  
Multi Agent ◽  
Pros And Cons ◽  
Planning Algorithms

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. In this work, we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of Safe Interval Path Planning (SIPP), a continuous time single agent planning algorithms, and Conflict-Based Search (CBS). We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.

SiMAMT

2017 ◽  
Vol 5 (1) ◽  
pp. 1-29
Author(s):  
D. Michael Franklin ◽  
Xiaolin Hu
Keyword(s):  
Formal Specification ◽  
Experimental Results ◽  
Strategy Selection ◽  
Multiple Agents ◽  
Strategy Evaluation ◽  
Simulation Strategy ◽  
Multi Agent ◽  
Multiple Levels ◽  
Inference Strategy ◽  
And Control

Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Additionally, similar structures can be seen in game environments, where agents work together as teams to compete with other teams. The different agents within the same team must, while maintaining their own “personality”, work together and coordinate with each other to achieve a common team goal. This paper develops strategy-based multi-agent multi-team systems, where strategy is framed as an instrument at the team level to coordinate the multiple agents of a team in a cohesive way. The authors present SiMAMT, a framework for strategy-based multi-agent multi-team systems. The different components of the framework, including strategy simulation, strategy inference, strategy evaluation, and strategy selection are described. A formal specification of strategy and strategy-based multi-agent multi-team systems is provided. An example and experimental results are given to illustrate the proposed framework and its efficacy.

Context-Based Entity Resolution

2014 ◽  
pp. 67-86
Keyword(s):  
High Precision ◽  
Entity Resolution ◽  
Efficient Algorithms ◽  
Experimental Results ◽  
Context Information ◽  
Prior Work ◽  
Insufficient Information ◽  
Data Objects ◽  
Er Algorithm

Prior work of entity resolution involves expensive similarity comparison and clustering approaches. Additionally, the quality of entity resolution may be low due to insufficient information. To address these problems, by adopting context information of data objects, the authors present a novel framework of entity resolution, Context-Based Entity Description (CED), to make context information help entity resolution. In this framework, each entity is described by a set of CEDs. During entity resolution, objects are only compared with CEDs to determine its corresponding entity. Additionally, the authors propose efficient algorithms for CED discovery, maintenance, and CED-based entity resolution. The authors experimentally evaluated the CED-based ER algorithm on the real DBLP datasets, and the experimental results show that this algorithm can achieve both high precision and recall as well as outperform existing methods.

THE THEORY OF SOME A-C. COMMUTATOR MOTORS WITH SERIES CHARACTERISTICS: I. THE REPULSION MOTOR

1942 ◽  
Vol 20a (5) ◽  
pp. 49-70
Author(s):  
E. G. Cullwick
Keyword(s):  
Power Factor ◽  
Maximum Power ◽  
Experimental Results ◽  
Finite Speed ◽  
Three Phase ◽  
Maximum Power Factor ◽  
Starting Torque ◽  
General Method

A general method, using "full reactances", is applied for developing the theory of the simple repulsion motor, the compensated repulsion motor, and the three-phase series motor. The effect of the currents induced in the armature turns short-circuited by the brushes is included, and is shown to affect profoundly the operation of the motors. Graphical constructions for the current loci are given, together with methods of measuring the various reactances, and of accounting for the effect of saturation. Experimental results for a three-phase series motor are included and compared with calculated values.Part I, published below, deals with the simple repulsion motor. Neglecting the effects of the coils short-circuited by the brushes, the usual well known results are obtained, and the position of the brushes for maximum starting torque is studied. The currents circulating in the coils short-circuited by the brushes are then found to have the following effects:(a) The performance of the motor, for a given current, is improved at speeds below synchronism, and is impaired at speeds above synchronism.(b) The maximum power factor is found to occur at some finite speed, whereas, if the effect of the short-circuited coils is neglected the power factor is a maximum at infinite speed.(c) The no-load speed is considerably lower than that usually associated with series motors.The rise and fall of the currents in the coils short-circuited by the brushes is studied in the Appendix.

scholarly journals Combinatorial Auctions via Machine Learning-based Preference Elicitation

2018 ◽  
Author(s):  
Gianluca Brero ◽  
Benjamin Lubin ◽  
Sven Seuken
Keyword(s):  
Machine Learning ◽  
Value Function ◽  
Allocative Efficiency ◽  
Preference Elicitation ◽  
Combinatorial Auctions ◽  
Prior Work ◽  
Spectrum Auction

Combinatorial auctions (CAs) are used to allocate multiple items among bidders with complex valuations. Since the value space grows exponentially in the number of items, it is impossible for bidders to report their full value function even in medium-sized settings. Prior work has shown that current designs often fail to elicit the most relevant values of the bidders, thus leading to inefficiencies. We address this problem by introducing a machine learning-based elicitation algorithm to identify which values to query from the bidders. Based on this elicitation paradigm we design a new CA mechanism we call PVM, where payments are determined so that bidders’ incentives are aligned with allocative efficiency. We validate PVM experimentally in several spectrum auction domains, and we show that it achieves high allocative efficiency even when only few values are elicited from the bidders.

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