12,663 research outputs found
Adaptable processes
We propose the concept of adaptable processes as a way of overcoming the
limitations that process calculi have for describing patterns of dynamic
process evolution. Such patterns rely on direct ways of controlling the
behavior and location of running processes, and so they are at the heart of the
adaptation capabilities present in many modern concurrent systems. Adaptable
processes have a location and are sensible to actions of dynamic update at
runtime; this allows to express a wide range of evolvability patterns for
concurrent processes. We introduce a core calculus of adaptable processes and
propose two verification problems for them: bounded and eventual adaptation.
While the former ensures that the number of consecutive erroneous states that
can be traversed during a computation is bound by some given number k, the
latter ensures that if the system enters into a state with errors then a state
without errors will be eventually reached. We study the (un)decidability of
these two problems in several variants of the calculus, which result from
considering dynamic and static topologies of adaptable processes as well as
different evolvability patterns. Rather than a specification language, our
calculus intends to be a basis for investigating the fundamental properties of
evolvable processes and for developing richer languages with evolvability
capabilities
Tree-oriented interactive processing with an application to theorem-proving, appendix E
The concept of unstructured structure editing and ted, an editor for unstructured trees, is described. Ted is used to manipulate hierarchies of information in an unrestricted manner. The tool was implemented and applied to the problem of organizing formal proofs. As a proof management tool, it maintains the validity of a proof and its constituent lemmas independently from the methods used to validate the proof. It includes an adaptable interface which may be used to invoke theorem provers and other aids to proof construction. Using ted, a user may construct, maintain, and verify formal proofs using a variety of theorem provers, proof checkers, and formatters
Adaptability Checking in Multi-Level Complex Systems
A hierarchical model for multi-level adaptive systems is built on two basic
levels: a lower behavioural level B accounting for the actual behaviour of the
system and an upper structural level S describing the adaptation dynamics of
the system. The behavioural level is modelled as a state machine and the
structural level as a higher-order system whose states have associated logical
formulas (constraints) over observables of the behavioural level. S is used to
capture the global and stable features of B, by a defining set of allowed
behaviours. The adaptation semantics is such that the upper S level imposes
constraints on the lower B level, which has to adapt whenever it no longer can
satisfy them. In this context, we introduce weak and strong adaptabil- ity,
i.e. the ability of a system to adapt for some evolution paths or for all
possible evolutions, respectively. We provide a relational characterisation for
these two notions and we show that adaptability checking, i.e. deciding if a
system is weak or strong adaptable, can be reduced to a CTL model checking
problem. We apply the model and the theoretical results to the case study of
motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte
A conceptual model for megaprogramming
Megaprogramming is component-based software engineering and life-cycle management. Magaprogramming and its relationship to other research initiatives (common prototyping system/common prototyping language, domain specific software architectures, and software understanding) are analyzed. The desirable attributes of megaprogramming software components are identified and a software development model and resulting prototype megaprogramming system (library interconnection language extended by annotated Ada) are described
On the Complexity of Case-Based Planning
We analyze the computational complexity of problems related to case-based
planning: planning when a plan for a similar instance is known, and planning
from a library of plans. We prove that planning from a single case has the same
complexity than generative planning (i.e., planning "from scratch"); using an
extended definition of cases, complexity is reduced if the domain stored in the
case is similar to the one to search plans for. Planning from a library of
cases is shown to have the same complexity. In both cases, the complexity of
planning remains, in the worst case, PSPACE-complete
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