31,199 research outputs found
Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations
In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns
Low-Cost Learning via Active Data Procurement
We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showing how to convert a large class of no-regret algorithms into
online posted-price and learning mechanisms. Our results in a sense parallel
classic sample complexity guarantees, but with the key resource being money
rather than quantity of data: With a budget constraint , we give robust risk
(predictive error) bounds on the order of . Because we use an
active approach, we can often guarantee to do significantly better by
leveraging correlations between costs and data.
Our algorithms and analysis go through a model of no-regret learning with
arriving pairs (cost, data) and a budget constraint of . Our regret bounds
for this model are on the order of and we give lower bounds on the
same order.Comment: Full version of EC 2015 paper. Color recommended for figures but
nonessential. 36 pages, of which 12 appendi
Statistics of the Kolkata Paise Restaurant Problem
We study the dynamics of a few stochastic learning strategies for the
'Kolkata Paise Restaurant' problem, where N agents choose among N equally
priced but differently ranked restaurants every evening such that each agent
tries get to dinner in the best restaurant (each serving only one customer and
the rest arriving there going without dinner that evening). We consider the
learning strategies to be similar for all the agents and assume that each
follow the same probabilistic or stochastic strategy dependent on the
information of the past successes in the game. We show that some 'naive'
strategies lead to much better utilization of the services than some relatively
'smarter' strategies. We also show that the service utilization fraction as
high as 0.80 can result for a stochastic strategy, where each agent sticks to
his past choice (independent of success achieved or not; with probability
decreasing inversely in the past crowd size). The numerical results for
utilization fraction of the services in some limiting cases are analytically
examined.Comment: 10 pages, 3 figs; accepted in New J Phy
Distributed Online Learning via Cooperative Contextual Bandits
In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems
Market-based Recommendation: Agents that Compete for Consumer Attention
The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains
The Kolkata Paise Restaurant Problem and Resource Utilization
We study the dynamics of the "Kolkata Paise Restaurant problem". The problem
is the following: In each period, N agents have to choose between N
restaurants. Agents have a common ranking of the restaurants. Restaurants can
only serve one customer. When more than one customer arrives at the same
restaurant, one customer is chosen at random and is served; the others do not
get the service. We first introduce the one-shot versions of the Kolkata Paise
Restaurant problem which we call one-shot KPR games. We then study the dynamics
of the Kolkata Paise Restaurant problem (which is a repeated game version of
any given one shot KPR game) for large N. For statistical analysis, we explore
the long time steady state behavior. In many such models with myopic agents we
get under-utilization of resources, that is, we get a lower aggregate payoff
compared to the social optimum. We study a number of myopic strategies,
focusing on the average occupation fraction of restaurants.Comment: revtex4, 8 pages, 3 figs, accepted in Physica
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