Search behavior of greedy best-first search

Greedy best-first search (GBFS) is a sibling of A* in the family of best-first state-space search algorithms. While A* is guaranteed to find optimal solutions of search problems, GBFS does not provide any guarantees but typically finds satisficing solutions more quickly than A*. A classical result of optimal best-first search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. Theoretical results of this kind are useful for the analysis of heuristics in different search domains and for the improvement of algorithms. For satisficing algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of GBFS in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by GBFS in sequence. High-water mark benches allow us to exactly determine the set of states that GBFS expands under at least one tie-breaking strategy. We show that benches contain craters. Once GBFS enters a crater, it has to expand every state in the crater before being able to escape. Benches and craters allow us to characterize the best-case and worst-case behavior of GBFS in given search instances. We show that computing the best-case or worst-case behavior of GBFS is NP-complete in general but can be computed in polynomial time for undirected state spaces. We present algorithms for extracting the set of states that GBFS potentially expands and for computing the best-case and worst-case behavior. We use the algorithms to analyze GBFS on benchmark tasks from planning competitions under a state-of-the-art heuristic. Experimental results reveal interesting characteristics of the heuristic on the given tasks and demonstrate the importance of tie-breaking in GBFS

The 2011 International Planning Competition

After a 3 years gap, the 2011 edition of the IPC involved a total of 55 planners, some of them versions of the same planner, distributed among four tracks: the sequential satisficing track (27 planners submitted out of 38 registered), the sequential multicore track (8 planners submitted out of 12 registered), the sequential optimal track (12 planners submitted out of 24 registered) and the temporal satisficing track (8 planners submitted out of 14 registered). Three more tracks were open to participation: temporal optimal, preferences satisficing and preferences optimal. Unfortunately the number of submitted planners did not allow these tracks to be finally included in the competition. A total of 55 people were participating, grouped in 31 teams. Participants came from Australia, Canada, China, France, Germany, India, Israel, Italy, Spain, UK and USA. For the sequential tracks 14 domains, with 20 problems each, were selected, while the temporal one had 12 domains, also with 20 problems each. Both new and past domains were included. As in previous competitions, domains and problems were unknown for participants and all the experimentation was carried out by the organizers. To run the competition a cluster of eleven 64-bits computers (Intel XEON 2.93 Ghz Quad core processor) using Linux was set up. Up to 1800 seconds, 6 GB of RAM memory and 750 GB of hard disk were available for each planner to solve a problem. This resulted in 7540 computing hours (about 315 days), plus a high number of hours devoted to preliminary experimentation with new domains, reruns and bugs fixing. The detailed results of the competition, the software used for automating most tasks, the source code of all the participating planners and the description of domains and problems can be found at the competition’s web page: http://www.plg.inf.uc3m.es/ipc2011-deterministicThis booklet summarizes the participants on the Deterministic Track of the International Planning Competition (IPC) 2011. Papers describing all the participating planners are included

Generation and exploitation of intermediate goals in automated planning

Mención Internacional en el título de doctorIn automated planning, domain-independent planners often scale poorly. This is due to the exponential blow up of the effort necessary to solve a planning task as its size increases. One of the most popular ways of addressing this problem is splitting the planning problem into several smaller ones. Each subproblem is in theory exponentially easier to solve than the original one, so planners that divide the original task will tend to scale much better. To divide the task into smaller ones, we need to find domain-independent methods to derive intermediate goals. In this thesis we will study different approaches that generate and exploit intermediate goals, without limiting ourselves to simply splitting the original problem. Three main lines of research will be pursued. The first one deals with regression, first tackling its shortcomings and then using it both in bidirectional search and as a way to derive novel heuristics based on intermediate goals. In the second one we propose sampling the search space randomly and using the randomly-sampled subgoals in a tree-like algorithms that effectively balances exploration and exploitation. Finally, in the third one we study the properties of the landmark graph, which represents precedence constraints among subgoals of the task. As a contribution, we propose different characterizations of the landmark graph that improve over its original formulation by providing more information, both formal properties of the task and finer orderings of subgoals exploitable by planners that already use landmarks. ----------------------------------------------------------En planificación automática, los planificadores independientes de dominio a menudo escalan pobremente. Esto se debe a la explosión exponencial del esfuerzo necesario para resolver una tarea de planificación según su tamaño incrementa. Uno de las formas más populares de abordar este problema es dividiendo el problema de planificación en varios problemas más pequeños. Para separar la tarea en tareas más pequeñas, hay que encontrar métodos independientes de dominio capaces de derivar metas intermedias. En esta tesis se estudiarán diferentes aproximaciones que generen y aprovechen metas intermedias, sin limitarnos a una mera subdivisión del problema original. Tres líneas de investigación serán exploradas. La primera trata sobre regresión, primero encarando sus limitaciones y después usándola tanto en búsqueda bidireccional como en nuevas heurísticas basadas en metas intermedias. En la segunda línea proponemos muestrear aleatoriamente el espacio de búsqueda y usar las submetas muestreadas aleatoriamente en un algoritmo basado en árboles aleatorios que balancea exploración y explotación de forma efectiva. Finalmente, en la tercera línea de investigación estudiamos las propiedades del grafo de landmarks, el cual representa las restricciones de precedencia entre submetas de la tarea. Como contribución, proponemos diferentes caracterizaciones del grafo de landmarks que mejoran su formulación original proporcionando más información, tanto propiedades formales de la tarea como ordenaciones de submetas más informadas aprovechables por planificadores que emplean landmarks.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Héctor Geffner.- Vocal: Joerg Hoffman

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року