11,604 research outputs found
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
Optimal Planning with State Constraints
In the classical planning model, state variables are assigned
values in the initial state and remain unchanged unless
explicitly affected by action effects. However, some properties
of states are more naturally modelled not as direct effects of
actions but instead as derived, in each state, from the primary
variables via a set of rules. We refer to those rules as state
constraints. The two types of state constraints that will be
discussed here are numeric state constraints and logical rules
that we will refer to as axioms.
When using state constraints we make a distinction between
primary variables, whose values are directly affected by action
effects, and secondary variables, whose values are determined by
state constraints. While primary variables have finite and
discrete domains, as in classical planning, there is no such
requirement for secondary variables. For example, using numeric
state constraints allows us to have secondary variables whose
values are real numbers. We show that state constraints are a
construct that lets us combine classical planning methods with
specialised solvers developed for other types of problems. For
example, introducing numeric state constraints enables us to
apply planning techniques in domains involving interconnected
physical systems, such as power networks.
To solve these types of problems optimally, we adapt commonly
used methods from optimal classical planning, namely state-space
search guided by admissible heuristics. In heuristics based on
monotonic relaxation, the idea is that in a relaxed state each
variable assumes a set of values instead of just a single value.
With state constraints, the challenge becomes to evaluate the
conditions, such as goals and action preconditions, that involve
secondary variables. We employ consistency checking tools to
evaluate whether these conditions are satisfied in the relaxed
state. In our work with numerical constraints we use linear
programming, while with axioms we use answer set programming and
three value semantics. This allows us to build a relaxed planning
graph and compute constraint-aware version of heuristics based on
monotonic relaxation.
We also adapt pattern database heuristics. We notice that an
abstract state can be thought of as a state in the monotonic
relaxation in which the variables in the pattern hold only one
value, while the variables not in the pattern simultaneously hold
all the values in their domains. This means that we can apply the
same technique for evaluating conditions on secondary variables
as we did for the monotonic relaxation and build pattern
databases similarly as it is done in classical planning.
To make better use of our heuristics, we modify the A* algorithm
by combining two techniques that were previously used
independently ā partial expansion and preferred operators. Our
modified algorithm, which we call PrefPEA, is most beneficial in
cases where heuristic is expensive to compute, but accurate, and
states have many successors
Extending classical planning with state constraints: Heuristics and search for optimal planning
We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning contextThis work was supported by ARC project DP140104219, āRobust AI Planning for Hybrid Systemsā, and in part by ARO grant W911NF1210471 and ONR grant N000141210430
Optimal Planning Modulo Theories
Planning for real-world applications requires algorithms and tools with the ability to handle the complexity such scenarios entail. However, meeting the needs of such applications poses substantial challenges, both representational and algorithmic. On the one hand, expressive languages are needed to build faithful models. On the other hand, efficient solving techniques that can support these languages need to be devised. A response to this challenge is underway, and the past few years witnessed a community effort towards more expressive languages, including decidable fragments of first-order theories. In this work we focus on planning with arithmetic theories and propose Optimal Planning Modulo Theories, a framework that attempts to provide efficient means of dealing with such problems. Leveraging generic Optimization Modulo Theories (OMT) solvers, we first present domain-specific encodings for optimal planning in complex logistic domains. We then present a more general, domain- independent formulation that allows to extend OMT planning to a broader class of well-studied numeric problems in planning. To the best of our knowledge, this is the first time OMT procedures are employed in domain-independent planning
Activity Planning for a Lunar Orbital Mission
This paper describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric Dust Environment Explorer). LADEEs science phase was performed in an equatorial, retrograde orbit around the Moon. The science observations were constrained with respect to key points in the spacecrafts orbit. We present the approach taken to reduce the complexity of the activity planning task in order to effectively perform it within the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the orbit-based science constraints, given their dynamic nature due to the continually updated orbit determination solution
The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables
Planning with numeric state variables has been a challenge for many years,
and was a part of the 3rd International Planning Competition (IPC-3). Currently
one of the most popular and successful algorithmic techniques in STRIPS
planning is to guide search by a heuristic function, where the heuristic is
based on relaxing the planning task by ignoring the delete lists of the
available actions. We present a natural extension of ``ignoring delete lists''
to numeric state variables, preserving the relevant theoretical properties of
the STRIPS relaxation under the condition that the numeric task at hand is
``monotonic''. We then identify a subset of the numeric IPC-3 competition
language, ``linear tasks'', where monotonicity can be achieved by
pre-processing. Based on that, we extend the algorithms used in the heuristic
planning system FF to linear tasks. The resulting system Metric-FF is,
according to the IPC-3 results which we discuss, one of the two currently most
efficient numeric planners
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