United Kingdom

We study the knapsack problem with group fairness constraints. The input of the problem consists of a knapsack of bounded capacity and a set of items. Each item belongs to a particular category and has an associated weight and value. The goal of this problem is to select a subset of items such that all categories are fairly represented, the total weight of the selected items does not exceed the capacity of the knapsack, and the total value is maximized. We study the fairness parameters such as the bounds on the total value of items from each category, the total weight of items from each category, and the total number of items from each category. We give approximation algorithms for these problems. We also give experimental validation for the efficiency of our algorithms. These fairness notions could also be extended to the min-knapsack problem.  The fair knapsack problems encompass various important problems, such as participatory budgeting, fair budget allocation, and advertising.

AAMAS 2021

Group Fairness for Knapsack Problems

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

technical paper

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This paper describes a demonstration setup that integrates cognitive agents with the latest W3C standardization efforts for the Web of Things (WoT). The conceptual foundations of the implemented system are the integration of cognitive agent abstractions with W3C Web Things, which are generic abstractions of devices and virtual services that provide agents with various interaction affordances (e.g., actions, events). Together with the W3C WoT Scripting API, which is an ECMAScript-compatible API for W3C WoT environments, these standards allow JavaScript-based agents to be deployed and to operate in heterogeneous WoT environments. The agents can then be effectively distributed across the physical-virtual space in a write once, run anywhere manner: we deploy agents across a heterogeneous information system landscape that includes Web servers, browser-based front-ends, and constrained devices (microcontrollers). The deployment only requires minor platform-specific adjustments to consider resource and performance limitations on constrained devices. As a running example, we demonstrate a semi-autonomous assembly scenario with human-in-the-loop support.

Autonomous Agents on the Edge of Things

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Sequential Ski Rental Problem

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Autonomous Agents on the Edge of Things

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Next from AAMAS 2021

Autonomous Agents on the Edge of Things

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Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

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