29,017 research outputs found
The line planning routing game
In this paper, we propose a novel algorithmic approach to solve line planning problems. To this end, we model the line planning problem as a game where the passengers are players which aim at minimizing individual objective functions composed of travel time, transfer penalties, and a share of the overall cost of the solution. To find equilibria of this routing game, we use a best-response algorithm. We investigate, under which conditions on the line planning model a passenger’s best-response can be calculated efficiently and which properties are needed to guarantee convergence of the best-response algorithm. Furthermore, we determine the price of anarchy which bounds the objective value of an equilibrium with respect to a system- optimal solution of the line planning problem. For problems where best-responses cannot be found efficiently, we propose heuristic methods. We demonstrate our findings on some small computational examples
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars
The collective of autonomous cars is expected to generate almost optimal
traffic. In this position paper we discuss the multi-agent models and the
verification results of the collective behaviour of autonomous cars. We argue
that non-cooperative autonomous adaptation cannot guarantee optimal behaviour.
The conjecture is that intention aware adaptation with a constraint on
simultaneous decision making has the potential to avoid unwanted behaviour. The
online routing game model is expected to be the basis to formally prove this
conjecture.Comment: In Proceedings FVAV 2017, arXiv:1709.0212
Stepwise investment plan optimization for large scale and multi-zonal transmission system expansion
This paper develops a long term transmission expansion optimization methodology taking the probabilistic nature of generation and demand, spatial aspects of transmission investments and different technologies into account. The developed methodology delivers a stepwise investment plan to achieve the optimal grid expansion for additional transmission capacity between different zones. In this paper, the optimization methodology is applied to the Spanish and French transmission systems for long term optimization of investments in interconnection capacity
From supply chains to demand networks. Agents in retailing: the electrical bazaar
A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version
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