New Zealand

Nadežda Jankelová (September 20th 2017). Strategic Decision Making and Its Importance in Small Corporations, Corporate Governance and Strategic Decision Making, Okechukwu Lawrence Emeagwali, IntechOpen, DOI: 10.5772/intechopen.68858.

Kufre Bassey (July 6th 2016). Sequential Optimization Model for Marine Oil Spill Control, Robust Control - Theoretical Models and Case Studies, Moises Rivas López and Wendy Flores-Fuentes, IntechOpen, DOI: 10.5772/63050.

&quot;In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved – the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services
can also help reduce carbon emissions (due to fewer vehicles on roads).
Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis,
cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any
guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests).&quot;

AAMAS 2020

Competitive Ratios for Online Multi-capacity Ridesharing

online matching

competitive ratio

sequential decision making

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

"In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved – the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services
can also help reduce carbon emissions (due to fewer vehicles on roads).
Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis,
cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any
guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests)."

technical paper

How about Explainability in Human-Agent Systems

Incentivised decision markets are mechanisms that allow selecting one action among a set of actions based on properly incentivised forecasts about the actions' consequences. Existing research on decision markets is based on scoring rules. Because scoring rules based decision markets involve two-side liabilities that can be difficult to track, we here study more convenient securities based decision markets. We present a decision market setting that prices securities using a cost function derived from a scoring rule. In such a decision market setting, traders will have the same expected utility as forecasters measured by the corresponding scoring rule. Moreover, we identify differences between scoring rules based decision markets and securities based decision markets in terms of actual payoffs. Lastly, we describe an insurance mechanism that can shift risk from the market creator to a risk-neutral third party.

Decision Markets Implementation

Moving target defense has emerged as a critical paradigm of protecting a vulnerable system against persistent and stealthy attacks. To protect a system, a defender proactively changes the system configurations to limit the exposure of security vulnerabilities to potential attackers. In doing so, the defender creates asymmetric uncertainty and complexity for the attackers, making it much harder for them to compromise the system. In practice, the defender incurs a switching cost for each migration of the system configurations. The switching cost usually depends on both the current configuration and the following configuration. Besides, different system configurations typically require a different amount of time for an attacker to exploit and attack. Therefore, a defender must simultaneously decide both the optimal sequences of system configurations and the optimal timing for switching. In this paper, we propose a Markov Stackelberg Game framework to precisely characterize the defender's spatial and temporal decision-making in the face of advanced attackers. We introduce a relative value iteration algorithm that computes the defender's optimal moving target defense strategies. Empirical evaluation on real-world problems demonstrates the advantages of the Markov Stackelberg game model for spatial-temporal moving target defense.

Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model

Introducing DCRAC, a deep reinforcement learning framework for solving multi-objective partially-observable problems. DCRAC follows a conditioned actor-critic approach in
learning the optimal policy.

DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

This tutorial will survey results on fairness in resource allocation. Starting from the classical setting of cake-cutting, the tutorial will explore the allocation of divisible and indivisible goods, public decision-making, and online resource allocation. The tutorial will include various formal definitions of fairness, relations among these definitions, and algorithms for (and complexity of) satisfying such definitions.

Recent Advances in Fair Resource Allocation Tutorial Part 1

A quadcopter can capture photos from vantage points unattainable for a human photographer, but teloperating it to a good viewpoint is a non-trivial task. Since humans are good at composing photos, the aim of our research is to leverage deep learning to create a customizable flight controller that can capture photos under the guidance of a human photographer. Our system, the Selfie Drone Stick, allows the user to assign a vantage point to the quadcopter based on the phone's sensors. The user takes a selfie with the phone once, and the quadcopter autonomously flies to the target viewpoint. The proliferation of open source deep learning models provided us with a large variety of options for the computer vision and flight control systems. This article describes three key innovations required to deploy the models on a real robot: 1) a new architecture for rapid object detection, DUNet; 2) an abstract state representation for transferring learning from simulation to the hardware platform; 3) reward shaping and staging paradigms for training a deep reinforcement learning controller. Without these improvements, we were unable to learn a flight controller that adequately supported the intuitive user interface.

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Cost Allocation in Ridesharing

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How about Explainability in Human-Agent Systems

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Cost Allocation in Ridesharing

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