Optimal Planning with Global Numerical State Constraints

Most of the key computational ideas in planning have been developed for simple planning languages where action preconditions and goals are conjunctions of propositional atoms. Preconditions and goals that do not fit into this form are normally converted into it either manually or automatically. In this work, we show that this modeling choice hides important structural information, resulting in poorer heuristics and weaker planning performance. From a theoretical point of view, we show that the direct generalization of relaxed planning graph heuristics to more expressive languages that implicitly allow conjunctions of atoms with more than one state variable leaves open a crisp gap, as it fails to properly account for the constraints over these variables. The simple propositional languages that are standard in planning do not remove this gap but “hide it under the rug” by forcing atoms to be of the form X=c, where c is a constant and X is a (usually boolean) state variable. Closing this gap in the computation of the relaxed planning graph for more expressive languages leads to a more accurate but intractable heuristic, yet a cost-effective tradeoff can be achieved using local forms of constraint propagation that result in better heuristics, better plans, and a more effective search. We show this empirically over a diverse set of illustrative examples using a fragment of the Functional STRIPS planning language.

Section: Relaxed Planning Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Computation in Planning: Better Heuristics from More Expressive Languages

Francès

Geffner

2015

“…To guarantee that distance constraints (reflected in the flying time) are respected, the states that violate the given constraints are pruned from the search space, in a very similar way to what (Ivankovic et al 2014) do using global numerical state constraints (but without considering them yet in the heuristic evaluation), or what the planner MBP (Bertoli et al 2001) does with problem invariants coded as the verification of invariant properties in the NuSMV model checker (Cimatti et al 2000). To track the distance flown, we use a numerical variable that is updated along with the state and which value is monitored at planning-time.…”

Section: Interleaving Planning and Executionmentioning

confidence: 99%

An Online Replanning Approach for Crop Fields Mapping with Autonomous UAVs

Albore¹,

Peyrard

Sabbadin

et al. 2015

For managing production at the scale of crop fields, maps of plant pests are used to support farmer decisions. Such maps are costly to obtain since they require intensive surveys in the field, most of the time performed by human annotators or with human-controlled Unmanned Aerial Vehicles (UAVs). In this paper, we look at the next challenge from an AI planning point of view: flying fully autonomous UAVs equipped with online sequential decision-making capabilities for pests sampling and mapping in crop fields. Following existing work, we use a Markov Random Field framework to represent knowledge about the uncertain map and its quality, in order to compute an optimised pest-sampling policy. Since this planning problem is Pspace hard, thus too hard to be exactly solved either offline or online, we propose an approach interleaving planning and execution, inspired by recent works on fault-tolerant planning. From past observations at a given time step, we compute a full plan consisting in a sequence of observed locations and expected observations untill the end of the pest-sampling phase. The plan is then applied until the number of actual observations that differ from expected ones exceeds a given threshold, which triggers a new replanning episode. Our planning method favourably compares on the problem of weed map construction against an existing greedy approach - the only one working online - while adding the advantage of being adapted to the autonomous UAVs' flying time constraints.

“…However, it is sometimes more natural to model some properties of states as derived, in each state, via a set of rules that we call state constraints. These can take a variety of forms, for example, logical axioms (Thiébaux, Hoffmann, and Nebel 2005) or numeric equations and inequalities (Ivankovic et al 2014;Haslum et al 2018). The latter is suited for domains that involve interconnected physical systems, such as power grids, transport systems or water networks.…”

mentioning

confidence: 99%

Planning with Global State Constraints and State-Dependent Action Costs

Ivankovic

Gordon

Haslum

2019

Self Cite

Planning with global state constraints is an extension of classical planning in which some properties of each state are derived via a set of equations, rules or constraints. This extension enables more elegant modelling of networked physical systems such as power grids. So far, research in this setting focused on domains where action costs are constant, rather than a function of a state in which the action is applied. This limitation prevents us from accurately specifying the objective in some real-world domains, leading to generation of suboptimal plans. For example, when reconfiguring a power network, we often need to temporarily leave some users without electricity for a certain amount of time, and in such circumstances it is desirable to reduce the unsupplied load over the total time span. This preference can be expressed using statedependent action costs. We extend planning with global state constraints to include state-dependent action costs, adapt abstraction heuristics to this setting, and show improved performance on a set of problems.