Multi-Agent Continuous Transportation with Online Balanced Partitioning Artificial Intelligence

We introduce the concept of continuous transportation task to the context of multi-agent systems. A continuous transportation task is one in which a multi-agent team visits a number of fixed locations, picks up objects, and delivers them to a final destination. The goal is to maximize the rate of transportation while the objects are replenished over time. Examples of problems that need continuous transportation are foraging, area sweeping, and first/last mile problem. Previous approaches typically neglect the interference and are highly dependent on communications among agents. Some also incorporate an additional reconnaissance agent to gather information. In this paper, we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi-agent team without the need for a reconnaissance agent. We contribute two partitioning-transportation algorithms inspired by existing algorithms, and contribute one novel online partitioning-transportation algorithm with information gathering in the multi-agent team. Our algorithms have been implemented and tested extensively in the simulation. The results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms, even without any communications between the agents and without the presence of a reconnaissance agent.


AAAI Conferences

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed de- terministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, when a taxi is hired by a customer, its movements are uncontrolled and depend on (a) the customers requirement; and (b) the location of other taxis in the fleet. Towards addressing decision support in such problems, we make two key contributions: (a) A framework to represent the decision problem for selfish individuals in a dynamic population, where there is transitional uncertainty (involuntary movements); and (b) Two techniques (Fictitious Play for Symmetric Agent Populations, FP-SAP and Soft- max based Flow Update, SMFU) that converge to equilibrium solutions. We show that our techniques (apart from providing equilibrium strategies) outperform "driver" strategies with re- spect to overall availability of taxis and the revenue obtained by the taxi drivers. We demonstrate this on a real world data set with 8,000 taxis and 83 zones (representing the entire area of Singapore).

Entropy Controlled Non-Stationarity for Improving Performance of Independent Learners in Anonymous MARL Settings Machine Learning

With the advent of sequential matching (of supply and demand) systems (uber, Lyft, Grab for taxis; ubereats, deliveroo, etc for food; amazon prime, lazada etc. for groceries) across many online and offline services, individuals (taxi drivers, delivery boys, delivery van drivers, etc.) earn more by being at the "right" place at the "right" time. We focus on learning techniques for providing guidance (on right locations to be at right times) to individuals in the presence of other "learning" individuals. Interactions between indivduals are anonymous, i.e, the outcome of an interaction (competing for demand) is independent of the identity of the agents and therefore we refer to these as Anonymous MARL settings. Existing research of relevance is on independent learning using Reinforcement Learning (RL) or on Multi-Agent Reinforcement Learning (MARL). The number of individuals in aggregation systems is extremely large and individuals have their own selfish interest (of maximising revenue). Therefore, traditional MARL approaches are either not scalable or assumptions of common objective or action coordination are not viable. In this paper, we focus on improving performance of independent reinforcement learners, specifically the popular Deep Q-Networks (DQN) and Advantage Actor Critic (A2C) approaches by exploiting anonymity. Specifically, we control non-stationarity introduced by other agents using entropy of agent density distribution. We demonstrate a significant improvement in revenue for individuals and for all agents together with our learners on a generic experimental set up for aggregation systems and a real world taxi dataset.

7 things to never say on a plane

FOX News

Be careful what you say when you're on a plane. We all know free speech is not an absolute; Justice Oliver Wendell Holmes' argument about "falsely shouting fire in a theatre" is Exhibit A. There are also things you cannot say on a plane; doing so may not land you in front of the Supreme Court but it could get you kicked off your flight. There are millions of variations of this joke, but none of them are funny, not in airports and not on planes. The TSA has had to pull many such comedians out of security lines and occasionally arrest them; it happened to an NFL player last year. Frontier Airlines has allowed tipping on the food and beverage sales by flight attendants for the past three years -- tips that were then pooled with the rest of the crew.

Cruise Line to Create up to 200 Jobs in North Carolina

U.S. News

The Winston-Salem Journal reports agents, who will work in the Triad area, will serve in entry-level positions and will be the first point of contact for guests and travel agents looking to arrange vacations on the cruise line. It is the first time Norwegian Cruise Line has expanded its at-home agent program outside of Arizona and Florida.