110 research outputs found
PDDL2.1: An extension of PDDL for expressing temporal planning domains
In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community
TALplanner in IPC-2002: Extensions and Control Rules
TALplanner is a forward-chaining planner that relies on domain knowledge in
the shape of temporal logic formulas in order to prune irrelevant parts of the
search space. TALplanner recently participated in the third International
Planning Competition, which had a clear emphasis on increasing the complexity
of the problem domains being used as benchmark tests and the expressivity
required to represent these domains in a planning system. Like many other
planners, TALplanner had support for some but not all aspects of this increase
in expressivity, and a number of changes to the planner were required. After a
short introduction to TALplanner, this article describes some of the changes
that were made before and during the competition. We also describe the process
of introducing suitable domain knowledge for several of the competition
domains
Short Term Unit Commitment as a Planning Problem
‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time.
Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time.
Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation.
Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation.
Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
In recent years research in the planning community has moved increasingly
toward s application of planners to realistic problems involving both time and
many typ es of resources. For example, interest in planning demonstrated by the
space res earch community has inspired work in observation scheduling,
planetary rover ex ploration and spacecraft control domains. Other temporal and
resource-intensive domains including logistics planning, plant control and
manufacturing have also helped to focus the community on the modelling and
reasoning issues that must be confronted to make planning technology meet the
challenges of application. The International Planning Competitions have acted
as an important motivating fo rce behind the progress that has been made in
planning since 1998. The third com petition (held in 2002) set the planning
community the challenge of handling tim e and numeric resources. This
necessitated the development of a modelling langua ge capable of expressing
temporal and numeric properties of planning domains. In this paper we describe
the language, PDDL2.1, that was used in the competition. We describe the syntax
of the language, its formal semantics and the validation of concurrent plans.
We observe that PDDL2.1 has considerable modelling power --- exceeding the
capabilities of current planning technology --- and presents a number of
important challenges to the research community
Modelling Mixed Discrete-Continuous Domains for Planning
In this paper we present pddl+, a planning domain description language for
modelling mixed discrete-continuous planning domains. We describe the syntax
and modelling style of pddl+, showing that the language makes convenient the
modelling of complex time-dependent effects. We provide a formal semantics for
pddl+ by mapping planning instances into constructs of hybrid automata. Using
the syntax of HAs as our semantic model we construct a semantic mapping to
labelled transition systems to complete the formal interpretation of pddl+
planning instances. An advantage of building a mapping from pddl+ to HA theory
is that it forms a bridge between the Planning and Real Time Systems research
communities. One consequence is that we can expect to make use of some of the
theoretical properties of HAs. For example, for a restricted class of HAs the
Reachability problem (which is equivalent to Plan Existence) is decidable.
pddl+ provides an alternative to the continuous durative action model of
pddl2.1, adding a more flexible and robust model of time-dependent behaviour
CASP Solutions for Planning in Hybrid Domains
CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
In recent years research in the planning community has moved increasingly
toward s application of planners to realistic problems involving both time and
many typ es of resources. For example, interest in planning demonstrated by the
space res earch community has inspired work in observation scheduling,
planetary rover ex ploration and spacecraft control domains. Other temporal and
resource-intensive domains including logistics planning, plant control and
manufacturing have also helped to focus the community on the modelling and
reasoning issues that must be confronted to make planning technology meet the
challenges of application. The International Planning Competitions have acted
as an important motivating fo rce behind the progress that has been made in
planning since 1998. The third com petition (held in 2002) set the planning
community the challenge of handling tim e and numeric resources. This
necessitated the development of a modelling langua ge capable of expressing
temporal and numeric properties of planning domains. In this paper we describe
the language, PDDL2.1, that was used in the competition. We describe the syntax
of the language, its formal semantics and the validation of concurrent plans.
We observe that PDDL2.1 has considerable modelling power --- exceeding the
capabilities of current planning technology --- and presents a number of
important challenges to the research community
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Temporal planning with constants in context
Required concurrency can cause actions to interfere with running
continuous effects. This interference can modify the rate of change,
including the polarity, of a continuous effect. In this work, we
propose a mechanism to support discrete interference of rates of
change caused by instantaneous actions, the start and end endpoints
of other durative actions, and numeric timed initial fluents.
Current temporal planners have very limited support for such
numeric dynamics. COLIN reduces a temporal numeric planning
problem to a linear program (LP), but operates on an implicit
assumption that the rate of change of a durative action’s continuous
effect is constant throughout its execution. In this work we propose
some enhancements to the algorithms used in COLIN, in order
to support discrete interference of continuous effects, and a new
planner, DICE, was developed to implement them.peer-reviewe
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