36,821 research outputs found
Modeling Cooperative Navigation in Dense Human Crowds
For robots to be a part of our daily life, they need to be able to navigate
among crowds not only safely but also in a socially compliant fashion. This is
a challenging problem because humans tend to navigate by implicitly cooperating
with one another to avoid collisions, while heading toward their respective
destinations. Previous approaches have used hand-crafted functions based on
proximity to model human-human and human-robot interactions. However, these
approaches can only model simple interactions and fail to generalize for
complex crowded settings. In this paper, we develop an approach that models the
joint distribution over future trajectories of all interacting agents in the
crowd, through a local interaction model that we train using real human
trajectory data. The interaction model infers the velocity of each agent based
on the spatial orientation of other agents in his vicinity. During prediction,
our approach infers the goal of the agent from its past trajectory and uses the
learned model to predict its future trajectory. We demonstrate the performance
of our method against a state-of-the-art approach on a public dataset and show
that our model outperforms when predicting future trajectories for longer
horizons.Comment: Accepted at ICRA 201
Effects of Anticipation in Individually Motivated Behaviour on Control and Survival in a Multi-Agent Scenario with Resource Constraints
This is an open access article distributed under the Creative Commons Attribution License CC BY 3.0 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Self-organization and survival are inextricably bound to an agent’s ability to control and anticipate its environment. Here we assess both skills when multiple agents compete for a scarce resource. Drawing on insights from psychology, microsociology and control theory, we examine how different assumptions about the behaviour of an agent’s peers in the anticipation process affect subjective control and survival strategies. To quantify control and drive behaviour, we use the recently developed information-theoretic quantity of empowerment with the principle of empowerment maximization. In two experiments involving extensive simulations, we show that agents develop risk-seeking, risk-averse and mixed strategies, which correspond to greedy, parsimonious and mixed behaviour. Although the principle of empowerment maximization is highly generic, the emerging strategies are consistent with what one would expect from rational individuals with dedicated utility models. Our results support empowerment maximization as a universal drive for guided self-organization in collective agent systemsPeer reviewedFinal Published versio
Dynamic Bayesian Predictive Synthesis in Time Series Forecasting
We discuss model and forecast combination in time series forecasting. A
foundational Bayesian perspective based on agent opinion analysis theory
defines a new framework for density forecast combination, and encompasses
several existing forecast pooling methods. We develop a novel class of dynamic
latent factor models for time series forecast synthesis; simulation-based
computation enables implementation. These models can dynamically adapt to
time-varying biases, miscalibration and inter-dependencies among multiple
models or forecasters. A macroeconomic forecasting study highlights the dynamic
relationships among synthesized forecast densities, as well as the potential
for improved forecast accuracy at multiple horizons
Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting
We develop the methodology and a detailed case study in use of a class of
Bayesian predictive synthesis (BPS) models for multivariate time series
forecasting. This extends the recently introduced foundational framework of BPS
to the multivariate setting, with detailed application in the topical and
challenging context of multi-step macroeconomic forecasting in a monetary
policy setting. BPS evaluates-- sequentially and adaptively over time-- varying
forecast biases and facets of miscalibration of individual forecast densities,
and-- critically-- of time-varying inter-dependencies among them over multiple
series. We develop new BPS methodology for a specific subclass of the dynamic
multivariate latent factor models implied by BPS theory. Structured dynamic
latent factor BPS is here motivated by the application context-- sequential
forecasting of multiple US macroeconomic time series with forecasts generated
from several traditional econometric time series models. The case study
highlights the potential of BPS to improve of forecasts of multiple series at
multiple forecast horizons, and its use in learning dynamic relationships among
forecasting models or agents
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management
Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation
Asset Pricing Model with Heterogeneous Investment Horizons
In this paper we study the dynamics of a simple asset pricing model describing the trading activity of heterogeneous agents in a "stylized" market. The economy in the model contains two assets: a bond with risk-less return and a dividend paying stock. The price of the stock is determined through market clearing condition. Traders are speculators described as expected utility maximizers with heterogeneous beliefs about future stock price and with heterogeneous estimation of risk. In particular, we consider traders who base their investment decision on different time horizons and we analyze the effect of these differences on the price dynamics. Under suitable parameterization, the stock no-arbitrage "fundamental" price can emerge as a stable fixed point of the model dynamics. For different parameterizations, however, the market shows cyclical or chaotic price dynamics with speculative bubbles and crashes. We find that the sole heterogeneity of agents with respect to their time horizons is not enough to guarantee the instability of the fundamental price and the emergence of non-trivial price dynamics. However, if different groups of agents are characterized by different trading behaviors, the introduction of heterogeneous investment horizons can help to decrease the stability region of the "fundamental" fixed point. The role of time horizons turns out to be different for different trade behaviors and, in general, depends on the whole ecology of agents' beliefs. We demonstrate this effect discussing a case in which the increase of fundamentalists time horizons can lead to cyclical or chaotic price behavior, while the same increase for the chartists helps to stabilize the fundamental price.Asset Pricing, Heterogeneous Beliefs, Investment Horizons
The Impact of Simple Institutions in Experimental Economies with Poverty Traps
We introduce an experimental approach to study the effect of institutions on economic growth. In
each period, agents produce and trade output in a market, and allocate it to consumption and
investment. Productivity is higher if total capital stock is above a threshold. The threshold externality
generates two steady states – a suboptimal poverty trap and an optimal steady state. In a baseline
treatment, the economies converge to the poverty trap. However, the ability to make public
announcements or to vote on competing and binding policies, increases output, welfare and capital
stock. Combining these two simple institutions guarantees that the economies escape the poverty
trap
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