The paper proposes a multi-agent method to creating an intelligent adaptive system of train real time scheduling with conflict limitations. The architecture of the multi-agent system consisting of two base planning subsystems is described. Subsystem interaction protocols and protocols of agent interaction within each subsystem are presented. The example of schedule planning in various situation...

Conversational recommender systems have been introduced in Travel and Tourism applications in order to support interactive dialogues which assist users in acquiring their goals, e.g., travel planning in a dynamic packaging system. Notwithstanding this increased interactivity, these systems employ an interaction strategy that is specified a priori (at design time) and is followed quite rigidly d...

Several sophisticated sampling strategies have been proposed recently to address the narrow passage problem for probabilistic roadmap (PRM) planning. They all have unique strengths and weaknesses in different environments, but none seems sufficient on its own in general. In this paper, we propose a systematic approach for adaptively combining multiple sampling strategies for PRM planning. Using...

The paper presents an experiment on spatial planning in 2D maze-like environments in which participants had to build and execute on-line a plan to reach a target. The experiment manipulated three factors: type of instructions (two levels), number of obstacles in the mazes (four levels), and length of the minimum path to the target (three levels). The instructions were conceived to vary the impo...

This paper describes a friendly graphical interface for HAP, a rule-configurable planning system, which automatically adapts to each problem, in order to achieve best performance. HAP analyzes the problem and uses a rule system in order to make the most appropriate choices for planning. The graphical interface enables the user to use and even interfere in the process of fine tuning the planning...

This research is concerned with problems where an agent is situated in a stochastic world without prior knowledge of the world’s dynamics. The agent must act in such a way so as to maximize its expected discounted reward over time. The state and action spaces are extremely large or infinite, and control decisions are made in continuous time. The objective of this research is to create a system ...

We consider the problem of information collection from an environment by a multi-robot system, where the locations to sample information from are known with some amount of uncertainty by the robots. Different robots are equipped with different types of sensors and the quality of information collected can be improved if information is sampled from the environment with different sensors, wherever...

To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful robot configurations is learned to support faster planning. More generally, the algorit...

Service-Oriented Computing is a cornerstone for the realization of user needs through the automatic composition of services from service descriptions and user tasks, i.e., high-level descriptions of the user needs. Yet, automatic service composition processes commonly assume that service descriptions and user tasks share the same abstraction level, and that services have been pre-designed to in...