24 research outputs found
Personalized Tourist Route Generation
When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time
UAV Mission Planning: From Robust to Agile
Unmanned Aerial Vehicles (UAVs) are important assets for information gathering in Intelligence Surveillance and Reconnaissance (ISR) missions. Depending on the uncertainty in the planning parameters, the complexity of the mission and its constraints and requirements, different planning methods might be preferred. The first two planning approaches that we will discuss, deal with uncertainty in fuel consumption of the UAV. The third planning approach is designed for an even more uncertain and dynamic situation in which travel and recording times are stochastic, time windows are associated to target locations and new targets become of interest during the flight of the UAV. As such, the proposed approaches gradually move from robust to agile as the uncertainty and dynamicity in the problems increases
Anytime and efficient coalition formation with spatial and temporal constraints
The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP)
is a multi-agent task scheduling problem where the tasks are spatially
distributed, with deadlines and workloads, and the number of agents is
typically much smaller than the number of tasks, thus the agents have to form
coalitions in order to maximise the number of completed tasks. The current
state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA)
algorithm, has two main limitations. First, its time complexity is exponential
with the number of agents. Second, as we show, its look-ahead technique is not
effective in real-world scenarios, such as open multi-agent systems, where new
tasks can appear at any time. In this work, we study its design and define an
extension, called Coalition Formation with Improved Look-Ahead (CFLA2), which
achieves better performance. Since we cannot eliminate the limitations of CFLA
in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be
anytime, efficient and with provable guarantees, called Cluster-based Coalition
Formation (CCF). We empirically show that, in settings where the look-ahead
technique is highly effective, CCF completes up to 30% (resp. 10%) more tasks
than CFLA (resp. CFLA2) while being up to four orders of magnitude faster. Our
results affirm CCF as the new state-of-the-art algorithm to solve the CFSTP.Comment: 18 pages, 1 figur