12 research outputs found
Zone-based formal specification and timing analysis of real-time self-adaptive systems
Self-adaptive software systems are able to autonomously adapt their behavior at run-time to react to internal
dynamics and to uncertain and changing environment conditions. Formal specification and verification
of self-adaptive systems are tasks generally very difficult to carry out, especially when involving time constraints.
In this case, in fact, the system correctness depends also on the time associated with events.
This article introduces the Zone-based Time Basic Petri nets specification formalism. The formalism
adopts timed adaptation models to specify self-adaptive behavior with temporal constraints, and relies on
a zone-based modeling approach to support separation of concerns. Zones identified during the modeling
phase can be then used as modules either in isolation, to verify intra-zone properties, or all together, to verify
inter-zone properties over the entire system. In addition, the framework allows the verification of (timed)
robustness properties to guarantee self-healing capabilities when higher levels of reliability and availability
are required to the system, especially when dealing with time-critical systems. This article presents also
the ZAFETY tool, a Java software implementation of the proposed framework, and the validation and
experimental results obtained in modeling and verifying two time-critical self-adaptive systems: the Gas
Burner system and the Unmanned Aerial Vehicle system
Enhancing Context Specifications for Dependable Adaptive Systems: A Data Mining Approach
Context: Adaptive systems are expected to cater for various operational contexts by having multiple strategies in achieving their objectives and the logic for
matching strategies to an actual context. The prediction of relevant contexts at
design time is paramount for dependability. With the current trend on using data
mining to support the requirements engineering process, this task of understanding context for adaptive system at design time can benefit from such techniques
as well.
Objective: The objective is to provide a method to refine the specification of
contextual variables and their relation to strategies for dependability. This refinement shall detect dependencies between such variables, priorities in monitoring
them, and decide on their relevance in choosing the right strategy in a decision
tree.
Method: Our requirements-driven approach adopts the contextual goal modelling structure in addition to the operationalization values of sensed information
to map contexts to the system’s behaviour. We propose a design time analysis process using a subset of data mining algorithms to extract a list of relevant contexts
and their related variables, tasks, and/or goals.
Results: We experimentally evaluated our proposal on a Body Sensor Network
system (BSN), simulating 12 resources that could lead to a variability space of
4096 possible context conditions. Our approach was able to elicit subtle contexts that would significantly affect the service provided to assisted patients and
relations between contexts, assisting the decision on their need, and priority in
monitoring.
Conclusion: The use of some data mining techniques can mitigate the lack
of precise definition of contexts and their relation to system strategies for dependability. Our method is practical and supportive to traditional requirements
specification methods, which typically require intense human intervention
Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives
Collectiveness is an important property of many systems--both natural and
artificial. By exploiting a large number of individuals, it is often possible
to produce effects that go far beyond the capabilities of the smartest
individuals, or even to produce intelligent collective behaviour out of
not-so-intelligent individuals. Indeed, collective intelligence, namely the
capability of a group to act collectively in a seemingly intelligent way, is
increasingly often a design goal of engineered computational systems--motivated
by recent techno-scientific trends like the Internet of Things, swarm robotics,
and crowd computing, just to name a few. For several years, the collective
intelligence observed in natural and artificial systems has served as a source
of inspiration for engineering ideas, models, and mechanisms. Today, artificial
and computational collective intelligence are recognised research topics,
spanning various techniques, kinds of target systems, and application domains.
However, there is still a lot of fragmentation in the research panorama of the
topic within computer science, and the verticality of most communities and
contributions makes it difficult to extract the core underlying ideas and
frames of reference. The challenge is to identify, place in a common structure,
and ultimately connect the different areas and methods addressing intelligent
collectives. To address this gap, this paper considers a set of broad scoping
questions providing a map of collective intelligence research, mostly by the
point of view of computer scientists and engineers. Accordingly, it covers
preliminary notions, fundamental concepts, and the main research perspectives,
identifying opportunities and challenges for researchers on artificial and
computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for
publication in the Artificial Life journal. Data: 34 pages, 2 figure