Learning in planning with temporally extended goals and uncontrollable events

Recent contributions to advancing planning from the classical model to more realistic problems include using temporal logic such as LTL to express desired properties of a solution plan. This paper introduces a planning model that combines temporally extended goals and uncontrollable events. The planning task is to reach a state such that all event sequences generated from that state satisfy the problem's temporally extended goal. A real-life application that motivates this work is to use planning to configure a system in such a way that its subsequent, non-deterministic internal evolution (nominal behavior) is guaranteed to satisfy a condition expressed in temporal logic. A solving architecture is presented that combines planning, model checking and learning. An online learning process incrementally discovers information about the problem instance at hand. The learned information is useful both to guide the search in planning and to safely avoid unnecessary calls to the model checking module. A detailed experimental analysis of the approach presented in this paper is included. The new method for online learning is shown to greatly improve the system performance.NICTA is funded by the Australian Government’s Department of Communications, Information Technology, and the Arts and the Australian Research Council through Backing Australia’s Ability and the ICT Research Centre of Excellence program

Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators

Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmanns state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred

The management of Phomopsis viticola in Tarnave vineyards

Phomopsis viticola, in poorly managed vineyards, can produce a lot of damage due to its year-to-year infection pattern. In combination with a high spore reserve from the previous year, this causes reduced grape yields. This study aims to assess the Phomopsis viticola attack in Tarnave vineyards during May-July 2020. The data (frequency and intensity of the attack) was collected from Craciunelu de Jos, and the attack degree (AD) was calculated before and after the treatments applied to 'Saugvinon blanc', 'Riesling italian', 'Fetească regală' and 'Traminer roz' grapevine cultivars. The AD before the treatment with contact and systemic products had values between 27.80% and 4.10%, after the treatments of the first period (MayJune 2020) the AD was reduced at values between 3.40% and 0.83% and after the treatments of the second period (June-July 2020) the AD had the values between 9.00% and 12.40%. In Tarnave vineyards, 'Fetească regală', 'Sauvignion blanc' and Riesling italian cultivars have shown a higher susceptibility to the Phomopsis viticola attack and Traminer roz' a lower one. The fungicide treatments administrated were efficient in managing Phomopsis viticola in vineyards from Transylvania

A comparative study of navigation meshes

International audienceA navigation mesh is a representation of a 2D or 3D virtual environment that enables path planning and crowd simulation for walking characters. Various state-of-the-art navigation meshes exist, but there is no standardized way of evaluating or comparing them. Each implementation is in a different state of maturity, has been tested on different hardware, uses different example environments, and may have been designed with a different application in mind. In this paper, we conduct the first comparative study of navigation meshes. First, we give general definitions of 2D and 3D environments and navigation meshes. Second, we propose theoretical properties by which navigation meshes can be classified. Third, we introduce metrics by which the quality of a navigation mesh implementation can be measured objectively. Finally, we use these metrics to compare various state-of-the-art navigation meshes in a range of 2D and 3D environments. We expect that this work will set a new standard for the evaluation of navigation meshes, that it will help developers choose an appropriate navigation mesh for their application, and that it will steer future research on navigation meshes in interesting directions

Reports on the 2015 AAAI Workshop Program

Knowledge, Skill, and Behavior Transfer in Autonomous Robots: report on p. 9

Incremental Heuristic Search for Planning with Temporally Extended Goals and Uncontrollable Events

Planning with temporally extended goals and uncontrollable events has recently been introduced as a formal model for system reconfiguration problems. An important application is to automatically reconfigure a real-life system in such a way that its subsequent internal evolution is consistent with a temporal goal formula. In this paper we introduce an incremental search algorithm and a search-guidance heuristic, two generic planning enhancements. An initial problem is decomposed into a series of subproblems, providing two main ways of speeding up a search. Firstly, a subproblem focuses on a part of the initial goal. Secondly, a notion of action relevance allows to explore with higher priority actions that are heuristically considered to be more relevant to the subproblem at hand. Even though our techniques are more generally applicable, we restrict our attention to planning with temporally extended goals and uncontrollable events. Our ideas are implemented on top of a successful previous system that performs online learning to better guide planning and to safely avoid potentially expensive searches. In experiments, the system speed performance is further improved by a convincing margin