Tourism is both an important industry and popular leisure activity undertaken by millions
around the world. One important task for tourists is to plan and schedule tour itineraries that …

We achieved a new milestone in the difficult task of enabling agents to learn about their
environment autonomously. Our neuro-symbolic architecture is trained end-to-end to …

Recommending and planning tour itineraries are challenging and time-consuming for
tourists, hence they may seek tour operators for help. Traditionally tour operators have …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

In this work we model and discuss how we can achieve coordination between different bus
service lines. Key problem challenges are (a) the multiple conflicting priorities (on one hand …

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which
requires planning and executing routes under uncertainty for an autonomous agent. The …

Itinerary planning serves an important purpose for travelers navigating through a public
transit network. The uncertainty of travel times within the network must be considered since …

Recent work on Neural-Symbolic systems that learn the discrete planning model from
images has opened a promising direction for expanding the scope of Automated Planning …

This paper explores the possibilities and challenges of realizing a context-aware travel
planner with bidirectional information exchange between the actor and the traveller during …

Multimodal travel is a ubiquitous part of living in a city. The operation of modern urban
transportation networks can negatively be impacted by multiple factors, including poor traffic …