221,064 research outputs found
Proposing A Cyber-Physical Production Systems Framework Linking Factory Planning And Factory Operation
The challenges for industrial companies in the area of factory planning and operation are characterised on the one hand by permanently shortening product life cycles and increasing product diversity. Furthermore, the demand for ecologically sustainable processes is growing and the complexity of production systems is increasing due to higher product complexity. This results in a complex decision-making space for companies within factory planning and factory operation which is difficult to plan. The advancing digitalisation can bring a great opportunity here. Modelling and simulation can create greater transparency in the context of planning and operation, and processes can be designed to be ecologically sustainable and efficient. Currently, research approaches in the context of factory planning and operation are focussing on the application and use of digital methods and tools of the Digital Factory (DF). However, the application is limited to individual areas in factory planning or factory operation.
For this reason, this paper focuses on the design of a framework that addresses both factory planning and factory operation aspects and links them through modelling and simulation. Cyber-physical production systems (CPPS) can help here by mapping the individual modules within planning and operation using individual agents in agent-based simulation (AB). By linking planning and real data, the processes from planning and operation can be taken into account. From this, insights gained from planning can be simulated in an early phase and subjected to optimisation during operation. The cycle-oriented CPPS can be used on an ongoing basis by preparing the generic building blocks on the planning and operational sides through structured data acquisition and implementing them in the real world with the help of decision support from the virtual world
Answer Set Planning Under Action Costs
Recently, planning based on answer set programming has been proposed as an
approach towards realizing declarative planning systems. In this paper, we
present the language Kc, which extends the declarative planning language K by
action costs. Kc provides the notion of admissible and optimal plans, which are
plans whose overall action costs are within a given limit resp. minimum over
all plans (i.e., cheapest plans). As we demonstrate, this novel language allows
for expressing some nontrivial planning tasks in a declarative way.
Furthermore, it can be utilized for representing planning problems under other
optimality criteria, such as computing ``shortest'' plans (with the least
number of steps), and refinement combinations of cheapest and fastest plans. We
study complexity aspects of the language Kc and provide a transformation to
logic programs, such that planning problems are solved via answer set
programming. Furthermore, we report experimental results on selected problems.
Our experience is encouraging that answer set planning may be a valuable
approach to expressive planning systems in which intricate planning problems
can be naturally specified and solved
Constructing Conditional Plans by a Theorem-Prover
The research on conditional planning rejects the assumptions that there is no
uncertainty or incompleteness of knowledge with respect to the state and
changes of the system the plans operate on. Without these assumptions the
sequences of operations that achieve the goals depend on the initial state and
the outcomes of nondeterministic changes in the system. This setting raises the
questions of how to represent the plans and how to perform plan search. The
answers are quite different from those in the simpler classical framework. In
this paper, we approach conditional planning from a new viewpoint that is
motivated by the use of satisfiability algorithms in classical planning.
Translating conditional planning to formulae in the propositional logic is not
feasible because of inherent computational limitations. Instead, we translate
conditional planning to quantified Boolean formulae. We discuss three
formalizations of conditional planning as quantified Boolean formulae, and
present experimental results obtained with a theorem-prover
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
In this paper, we present a new task that investigates how people interact
with and make judgments about towers of blocks. In Experiment~1, participants
in the lab solved a series of problems in which they had to re-configure three
blocks from an initial to a final configuration. We recorded whether they used
one hand or two hands to do so. In Experiment~2, we asked participants online
to judge whether they think the person in the lab used one or two hands. The
results revealed a close correspondence between participants' actions in the
lab, and the mental simulations of participants online. To explain
participants' actions and mental simulations, we develop a model that plans
over a symbolic representation of the situation, executes the plan using a
geometric solver, and checks the plan's feasibility by taking into account the
physical constraints of the scene. Our model explains participants' actions and
judgments to a high degree of quantitative accuracy
A Parametric Hierarchical Planner for Experimenting Abstraction Techniques
This paper presents a parametric system, devised
and implemented to perform hierarchical planning
by delegating the actual search to an external
planner (the "parameter") at any level of abstraction,
including the ground one. Aimed at
giving a better insight of whether or not the exploitation
of abstract spaces can be used for
solving complex planning problems, comparisons
have been made between instances of the
hierarchical planner and their non hierarchical
counterparts. To improve the significance of the
results, three different planners have been selected
and used while performing experiments.
To facilitate the setting of experimental environments,
a novel semi-automatic technique,
used to generate abstraction hierarchies starting
from ground-level domain descriptions, is also
described
A Critical Look at the Abstraction Based on Macro-Operators
Abstraction can be an effective technique for dealing with
the complexity of planning tasks. This paper is aimed at assessing and
identifying in which cases abstraction can actually speed-up the overall
search. In fact, it is well known that the impact of abstraction on the
time spent to search for a solution of a planning problem can be positive
or negative, depending on several factors -including the number of objects
defined in the domain, the branching factor, and the plan length.
Experimental results highlight the role of such aspects on the overall performance
of an algorithm that performs the search at the ground-level
only, and compares them with the ones obtained by enforcing abstraction
Plot-based urbanism and urban morphometrics : measuring the evolution of blocks, street fronts and plots in cities
Generative urban design has been always conceived as a creation-centered process, i.e. a process mainly concerned with the creation phase of a spatial transformation. We argue that, though the way we create a space is important, how that space evolves in time is ways more important when it comes to providing livable places gifted by identity and sense of attachment. We are presenting in this paper this idea and its major consequences for urban design under the title of “Plot-Based Urbanism”. We will argue that however, in order for a place to be adaptable in time, the right structure must be provided “by design” from the outset. We conceive urban design as the activity aimed at designing that structure. The force that shapes (has always shaped) the adaptability in time of livable urban places is the restless activity of ordinary people doing their own ordinary business, a kind of participation to the common good, which has hardly been acknowledged as such, that we term “informal participation”. Investigating what spatial components belong to the spatial structure and how they relate to each other is of crucial importance for urban design and that is the scope of our research. In this paper a methodology to represent and measure form-related properties of streets, blocks, plots and buildings in cities is presented. Several dozens of urban blocks of different historic formation in Milan (IT) and Glasgow (UK) are surveyed and analyzed. Effort is posed to identify those spatial properties that are shared by clusters of cases in history and therefore constitute the set of spatial relationships that determine the morphological identity of places. To do so, we investigate the analogy that links the evolution of urban form as a cultural construct to that of living organisms, outlining a conceptual framework of reference for the further investigation of “the DNA of places”. In this sense, we identify in the year 1950 the nominal watershed that marks the first “speciation” in urban history and we find that factors of location/centrality, scale and street permeability are the main drivers of that transition towards the entirely new urban forms of contemporary cities
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
We propose a new declarative planning language, called K, which is based on
principles and methods of logic programming. In this language, transitions
between states of knowledge can be described, rather than transitions between
completely described states of the world, which makes the language well-suited
for planning under incomplete knowledge. Furthermore, it enables the use of
default principles in the planning process by supporting negation as failure.
Nonetheless, K also supports the representation of transitions between states
of the world (i.e., states of complete knowledge) as a special case, which
shows that the language is very flexible. As we demonstrate on particular
examples, the use of knowledge states may allow for a natural and compact
problem representation. We then provide a thorough analysis of the
computational complexity of K, and consider different planning problems,
including standard planning and secure planning (also known as conformant
planning) problems. We show that these problems have different complexities
under various restrictions, ranging from NP to NEXPTIME in the propositional
case. Our results form the theoretical basis for the DLV^K system, which
implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
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