In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Path planning for multiple robots is well studied in the AI and robotics communities. For a given discretized environment, robots need to find collision-free paths to a set of specified goal locations. Robots can be fully anonymous, non-anonymous, or organized in groups. Although powerful solvers for this abstract problem exist, they make simplifying assumptions by ignoring kinematic constraints, making it difficult to use the resulting plans on actual robots. In this paper, we present a solution which takes kinematic constraints, such as maximum velocities, into account, while guaranteeing a user-specified minimum safety distance between robots. We demonstrate our approach in simulation and on real robots in 2D and 3D environments.
[A]: Path Planning With Kinematic Constraints For Robot Groups


[Q]: In many domains it is desirable to assess the pref­ erences of users in a qualitative rather than quan­ titative way. Such representations of qualitative preference orderings form an important compo­ nent of automated decision tools. We propose a graphical representation of preferences that re­ flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep­ resentation is often compact and arguably natural. We describe several search algorithms for domi­ nance testing based on this representation; these algorithms are quite effective, especially in spe­ cific network topologies, such as chainand tree­ structured networks, as well as polytrees.
[A]: Reasoning With Conditional Ceteris Paribus Preference Statements


[Q]: It is well known that conditional indepen­ dence can be used to factorize a joint prob­ ability into a multiplication of conditional probabilities. This paper proposes a con­ structive definition of intercausal indepen­ dence, which can be used to further factorize a conditional probability. An inference algo­ rithm is developed, which makes use of both conditional independence and intercausal in­ dependence to reduce inference complexity in Bayesian networks.
[A]:
Intercausal Independence and Heterogeneous Factorization