Magazine article AI Magazine

The Fifth International Competition on Knowledge Engineering for Planning and Scheduling: Summary and Trends

Magazine article AI Magazine

The Fifth International Competition on Knowledge Engineering for Planning and Scheduling: Summary and Trends

Article excerpt

We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. The ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated planning and scheduling systems.

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The International Competition on Knowledge Engineering for Planning and Scheduling has been running since 2005 as a biennial event promoting the development and importance of the use of knowledge engineering methods and techniques within this area. The aim of the competition series is to foster developments in the knowledge-based and domain modeling aspects of automated planning, to accelerate knowledge engineering research, and to encourage the creation and sharing of prototype tools and software platforms that promise more rapid, accessible, and effective ways to construct reliable and efficient automated planning systems.

ICKEPS 2016 aimed specifically (1) to provide an interesting opportunity for researchers and students to experience the challenges of knowledge engineering; (2) to motivate the planning community to create and improve tools and techniques for supporting the main design phases of a planning domain model; and (3) to provide new interesting and challenging models that can be used for testing the performance of state-of-the-art planning engines. In order to achieve the mentioned aims, ICKEPS 2016 focused on on-site modeling of challenging scenarios, performed by small teams.

This article summarizes the ICKEPS held in 2016. More information about the competition, including complete scenario descriptions, can be found on the ICKEPS 2016 website. (1)

Format and Participants

ICKEPS 2016 format included two main stages: Onsite modeling and demonstration.

During the on-site modeling stage, each team received a set of scenarios description and had to exploit the available time for generating the corresponding models. Four scenarios were provided. Two of them--Star Trek, Rescue of Levaq, and Roundabout --required temporal constraints, while the other two--RPG and Match-Three, Harry!--only required classical reasoning. Participants were free to select the scenarios to tackle and had no restrictions on the number and type of tools that can be used. The only constraints were on the available time (six hours were given) and on the maximum size of teams (at most four members).

The day after the on-site modeling, each team had 10 minutes to present and demonstrate the aspects of the knowledge engineering process they exploited for encoding the scenarios. Specifically, teams were expected to discuss the division of work among team members, the tools used, key decisions taken during the encoding, and the issues they faced.

Teams were then ranked by a board of judges, which included Minh Do (NASA, USA), Simone Fratini (ESA, Germany), Ron Petrick (Heriot-Watt University, UK), Patricia Riddle (University of Auckland, New Zealand), and David Smith (NASA, USA). The evaluation process will be described in the corresponding section below. Noteworthy, judges were presented during the demonstrations session and had the opportunity to ask questions and discuss relevant aspects of the knowledge engineering process the teams followed.

The competition had two tracks: the PDDL track, where teams had to generate PDDL models using PDDL features up to those introduced in version 3.1, and the Open track, where teams could encode models in any other language. However, for the open track, participants were also required to provide a planner able to deal with the selected language. Sixteen people, divided into six teams, took part in the competition. One team entered the Open track, while the remaining five decided to participate in the PDDL track.

Participants came from institutions in Australia, Brazil, Canada, USA, Japan, and the United Kingdom. …

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