Self-adaptive smart spaces by proactive means–end reasoning


The ability of a system to change its behavior at run-time is one of the foundations for engineering intelligent environments. The vision of computing systems that can manage themselves is fascinating, but to date, it presents many intellectual challenges to face. Run-time goal-model artifacts represent a typical approach to communicate requirements to the system and open new directions for dealing with self-adaptation. This paper presents a theoretical framework and a general architecture for engineering self-adaptive smart spaces by breaking out some design-time constraints between goals and tasks. The architecture supports software evolution because goals may be changed during the application lifecycle. The architecture is responsible for configuring its components as the result of a decision-making algorithm working at the knowledge level. The approach is specifically suitable for developing smart space systems, promoting scalability and reusability. The proposed architecture is evaluated through the execution of a set of randomized stress tests.

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Correspondence to Luca Sabatucci.

Appendix: engineering the exhibition center with MUSA

Appendix: engineering the exhibition center with MUSA

MUSA (Middleware for User-driven Service Adaptation) [23] is a multi-agent system for the composition and orchestration of services in a distributed and open environment. It aims at providing run-time modification of the flow of events, dynamic hierarchies of services, and integration of user preferences together with a system for run-time monitoring of activities that is also able to deal with unexpected failures and optimization.

The middlewareFootnote 1 is coded in JASON [12], a declarative programming language based on the AgentSpeak language [51] and the BDI theory [14]. The state of an agent together with its knowledge of the operative environment is modeled through its belief base, expressed by logical predicates. Desires are states the agent wants to attain according to its perceptions and beliefs. When an agent adopts a plan, it transforms a desire to an intention to be pursued.

In JASON the specification of plans is strictly connected to the desire that triggers its execution. Therefore, we developed a high-level language (GoalSPEC [58]) with the twofold aim of (1) allowing the user to specify requirements in the form of goals and (2) supporting the idea of decoupling what the system has to do, and how it must do that.

The theory of self-knowledge and action [42] asserts an agent achieves a goal by doing some actions if the agent knows what the action is and it knows that doing the action would result in the goal being satisfied [40]. Therefore, we also defined a high-level language to specify system’s capabilities.

Since software agents are deployed in a distributed environment, MUSA implements a distributed version of Algorithms 1 and 2. The knowledge level is supported by goals’ and capabilities’ specifications that are translated from high-level languages into agent’s beliefs [55]. A configuration represents a contract among the agents specifying how to collaborate. Therefore, service composition is obtained at run-time, as the result of a self-organization phenomenon.

In the following, we detail the ingredients needed to achieve our purpose: the way we depict the problem domain using an ontology, a goal specification language that refers to ontological elements as keys for grounding the goals on the problem and, finally, a capability language that supports the separation between the abstract description and the concrete implementation.

The domain ontology description

Working at the knowledge level implies an ontology commitment between who develops Capabilities and who specifies Goals. An ontology is a specification of a conceptualization made for the purpose of enabling knowledge sharing and reuse [59]. An ontological commitment is an agreement to use a thesaurus of words in a way that is consistent with the theory specified by an ontology [33].

A Problem Ontology (PO) [21, 52] is a conceptual model (and a set of guidelines) used to create an ontological commitment to developing complex distributed systems [22]. This artifact aims at visualizing an ontology as a set of concepts, predicates, and actions and how these are related to one another. An example is shown in Fig. 8.

Fig. 8

Example of problem ontology for the exhibition center. Ontology elements represented without stereotypes are to be read as concepts by default

The metamodel of a PO artifact, inspired by the FIPA (Foundation for Intelligent Physical Agents) standard [46], is briefly summarized as follows:

  • a Concept is a general term commonly used in a broad sense to identify “anything about which something is said” [20] that has a unique meaning in a subject domain;

  • a Predicate is the expression of a property, a quality or a state of one (ore more) concept(s);

  • an Action is the cause of an event by an acting concept [41]);

  • a Position is a specialization of concept performing Actions;

  • finally, an Object represents physical or abstract things.

  • the relationship is-a (or is-a-subtype-of) defines which objects are classified by which class, thus creating taxonomies;

  • the relationship part-of (or the counterpart has-part) represents the structure by composition;

  • the relationship association establishes links between ontological elements.

A goal specification language

The GoalSPEC language [58] has been specifically designed for enabling runtime goal injection and software agent reasoning. It takes inspiration from languages for specifying requirements for adaptation, such as RELAX [62]. However, GoalSPEC is in line with Definition 2 and adopts a domain-independent core grammar with a basic set of keywords that may be extended with a domain ontology.

The main entity of the GoalSPEC language is the Goal that is wanted by some Subject and it is structurally composed of a Trigger Condition and a Final State. It is worth underlining that both Trigger Conditions and Final States must be expressed by using predicates defined in a domain ontology.

Some examples of GoalSPEC productions for the domain of the Exhibition Center are listed below. For a complete specification of the syntax of GoalSPEC, see  [58].

  1. 1.

    WHEN registered(Usr,Event) AND NOT attending(Urs,Event) THE system SHALL PRODUCE notification(Event,Usr)

  2. 2.

    BEFORE moving(Urs,Loc) AND exhibition_area(Loc) THE visitor SHALL PRODUCE moving(Urs,Reception) AND check-in(Urs)

We use uppercase for the keywords of the language, and lowercase for domain-specific predicates. Logical variables start with an uppercase letter. Goal 1 indicates that ‘if a visitor has registered for an event, the user will be notified about time-table until he goes to the event’. Goal 2 states that ‘A condition to enter the exhibition center area is to go to the reception and check-in’.

A capability specification language

There is an obvious need for a semantic-based language to describe agent capabilities in a common language in a way that (1) the agent knows how to execute the capability and (2) it knows the effects of executing it [42].

So far we use a refinement of LARKS [61] a language for advertisement and request for knowledge sharing used in the context of web services.

Fig. 9

A couple of capabilities described through the capability specification language

A Capability is made of two parts: an abstract description, and a concrete body implementation (a set of plans for executing the job). The abstract description is defined through the following fields: (1) Name: unique label used to refer to the capability, (2) Input/OutputParams: variables that is necessary to instantiate for the execution, (3) Constraints: structural constraints on input/output variables, (4) Pre/Post Condition: conditions that must hold in the current state of the world and in the final state of the world and finally, (5) Evolution: function \(evo:W \longrightarrow W\) as described in Section 2.2.

We do not provide any language for the body, leaving the choice of the specific technology to the developer. We frequently used Java to code this part of our capabilities because of the smooth integration with Jason on the one side and the flexibility in the invocation of web services on the other side.

Figure 9 shows two examples of capabilities. The Alert Sender capability that uses smartphones to advert about the event and provide alerts about the timetable. The second capability is the Cloud Calendar Check capability that interacts with a calendar application for retrieving information about an event timetable.

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Sabatucci, L., Cossentino, M. Self-adaptive smart spaces by proactive means–end reasoning. J Reliable Intell Environ 3, 159–175 (2017).

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  • Self-adaptive systems
  • Software architecture
  • Smart spaces