Context-sensitive Modeling of Input SourceConfiguration for Evolving Intelligent Systems

Dominik WachholderDepartment of Information Systems

Communications EngineeringKepler University Linz, Austria

Email: dominik.wachholder@jku.at

Chris StaryDepartment of Information Systems

Communications EngineeringKepler University Linz, Austria

Email: christian.stary@jku.at

Abstract—Today’s dynamic in system interaction and userbehavior is crucial for quality of operation and performance ofevolving intelligent systems. Changes in network topology and in-formation availability need to be made accessible instantaneouslynot only for evolving intelligent systems but also for peopleusing and configuring these systems in ubiquitous computingenvironments. To insure interoperability among these systems,we present a representation and runtime approach for modelinginput source configuration aligned to both user and automaticsystem invocation. It is based on input sources that are arrangeddynamically in their context using bigraphs. This particular rep-resentation approach enforces rigorous interoperable semanticsusing a high-level set of dynamic relationships. We demonstratethe effectiveness of bigraph-based modeling for dynamic smarthome control.

I. INTRODUCTION

Today’s organization of work and life is increasingly re-quiring flexible and dynamic designs, not only with respectto how activities and tasks are organized but also regardingevolving intelligent systems (eIS) that need to be responsiveto modifications emerging at runtime [1], [2]. This is especiallytrue when people have to be able to use and configure suchsystems in ubiquitous computing environments without beingcognitively and physically overloaded [3]. Consider an ever-changing and unpredictable network of sensors and devices forsmart home control, including temperature sensors, barometersand smart phones. Evolving intelligent systems are requiredto be able to take into account all information availableon the network, irrespective of whether a new input sourceemerges (e.g., smart phone) or information of a new type isprovided (e.g., camera). Such systems need to continuouslyevolve by means of adapting their input source configurationand reasoning model to improve quality of operation andperformance [4], [5].

Evolvability of (intelligent) systems concerns the ability to“alter their structure or function so as to adapt to changingcircumstances” [6], [7]. Hereby, such a system should keepsome identity, “while having the capability to retain certaincharacteristics of interests despite changes in its composition,topology, and environmental properties. The latter is termed asrobustness. In case changes are dynamically tolerated, a systemis termed as resilient. It should tolerate (or even profit from) theonset of unanticipated changes and environmental conditionsthat might otherwise cause a loss of acceptable service” [8].

Consequently, evolving1 intelligent systems should be resilientand robust as they develop in non-stationary environments (cf.[5]). In the context of evolving fuzzy systems, an importantsub-area of evolving intelligent systems, these characteristicsare referred to as stability and plasticity [9]. The focus hereis on finding the balance between stable converging solutions(stability) while fostering the ability to learn, that is to integratenew information (plasticity)2.

SBSA

(a) System Integration

SBSA

(b) System Interoperability

Figure 1: System Integration vs. Interoperability

For learning in non-stationary environments, evolving in-telligent systems build upon a data-driven approach [1]. Thefoundation of operation is based on autonomous externalinput sources providing continuous data streams dynamicallyarranged around the system. Efficient orchestration of het-erogeneous input sources, however, requires not only theirconsolidation, but also semantic interoperability to achieve acommon goal [11]. Orchestrated systems therefore need tokeep operating separately without having functional dependen-cies among each other, but still are required to have the abilityto cooperate with each other in order to accomplish a commongoal. Such systems are characterized as interoperable as theyagree upon a common way of interaction in order to collaborateand exchange information (cf. [12]). As such, they are distinctfrom integrated systems – see Figure 1 showing two systemsSA and SB . In the first case (cf. Figure 1a), both systems areintegrated as they share common parts of their functionality

1In the context of evolving intelligent systems, the term evolvable ratherpursues the idea of gradual system development than making use of conceptsfrom evolutionary algorithms, such as mutation and selection.

2The endeavor of finding the balance between stability and plasticity is alsoreferred to as stability-plasticity dilemma (cf. [10]).

with each other, e.g., the defuzzification component and therule base. In the second case (cf. Figure 1b), however, bothsystems have agreed on a common way of interaction retainingtheir independence in terms of operation. A typical example forinteroperable systems is the interaction among a TVNP-based3

device and a smart home controller. The pre-defined protocolmakes sure that data can be exchanged and interpreted withoutmaking the interacting components dependent on each other.In this manner, CCTV systems4 from any manufacturer canbe either integrated into or removed from the network withoutmaking the individual systems inoperative.

In the following, we briefly review shortcomings of existingdesign support approaches as related to the modeling of dy-namic input source configuration before introducing bigraph-based development.

II. THE QUALITIES OF BIGRAPHS

So far, updates to system behavior have only concernedthe reasoning model (e.g., new rules). Neither quantitative norqualitative aspects and characteristics of incoming informationfrom input sources are considered. The HIEM framework5,however, shows that the data pre-processing is also consideredrelevant in the process flow of evolving intelligent systems(cf. Figure 2). Hence, not only the reasoning model but alsoa flexible input source configuration is crucial to quality ofoperation and performance of evolving intelligent systems.Nevertheless, integrated modeling as well as representationof the structure and behavior of input sources including theircontextual conditions has been barely addressed (e.g., [13]).

Input Data (FromSystem Process)

Data Pre-Processing (Selection of Training Samples, Annotation,

Cleaning, …)

Model Updatewith new samples

(conv. EIS)

Model Interpretation Component

Improved EIS Model

(understandable for a Human)

Choice of Model Architecture

Human Feedback Integration Component

Human

Figure 2: HIEM Framework [13]

Although the term modeling is present in the literatureof evolving intelligent systems, it mostly refers to designactivities related to the reasoning models of these systems(cf. [14], [15], [16]). Recent work focuses on the gradualdevelopment of the systems themselves – the availability ofappropriate data streams is assumed and not further ques-tioned or detailed. Different learning and prediction approachesare applied to identify changing patterns in incoming datastreams (cf. [17], [4], [18], [2]). Hereby, the objective is todevelop soft computing models at runtime rather than havingto train systems with particular training data sets prior their

3TV Network Protocol4Closed-Circuit TeleVision5Human-Inspired Evolving Machines

application. Alternatively, evolutionary algorithms are used toenable continuous system development especially in complexenvironments [14]. In the domain of genetic fuzzy systems,an important sub-area of evolving intelligent systems, suchalgorithms operate on a knowledge base6 to enable the constantrefinement of rule-based decision models for evolutionarymatters (cf. [19], [4]). The fuzzy markup language as anextension to XML constitutes a first attempt to representevolving intelligent systems in an interoperable way. It en-counters interoperability barriers of fuzzy systems – hardwareand software – by means of a standardized representationformat. For instance, a membership function associated with aparticular fuzzy term (FuzzyTerm) is described by the FML tagTriangularShape. Three attributes (Param1, Param2, Param3)are required to be specified describing it’s properties. Due tothe focus on the characteristics of fuzzy systems, however, thegeneric applicability of this representation format to modelthe dynamic structure and behavior of input sources and theircontextual conditions is limited (cf. [20], [21]).

In order to overcome these limitations, formal approaches,such as process calculi for concurrent systems based onindependent systems (agents) (cf. [22]), progressed towardsrepresenting (ubiquitous) systems including location aware-ness. Bigraphs [23] model systems by means of their structure,behavior, and their environment. Intended to model ubiquitoussystems, the theory captures the respective concepts of agent,locality, connectivity, motion, and interaction. The selectionis attributed to the development rationale not only to imple-ment computing tasks but also to reflect informing activities,interaction, and communication. Grouping these concepts, thetheory introduces two domains – space and motion – withrespect to the structure and dynamics of evolving systems.The concepts of linking (connectivity) and placing (locality)of agents are central in terms of space, where agents representindependent components of systems. Motion and interactionare considered explicitly in the context of dynamics allowingbigraphs reconfiguring and adapting themselves by means ofreactive system behavior.

The most fundamental domain in the theory of bigraphsis the notion of space that allows to describe agents andtheir structure in evolving systems. In this regard, the termstructure is a hypernym subsuming the placing as well asthe linking of agents. On the one hand, the placing structuredescribes the location of agents within an ubiquitous system.The nesting of agents allows to indicate whether an agent islocated within or outside another agent. The linking structure,on the other hand, describes the interrelations of agents in-dependent of their locations (nesting). The incorporation ofdigital agents (e.g., digital files) is also allowed, as neither theplacing nor the linking structure is limited to physical agentsexclusively Figure 3 illustrates an evolving system showingagents of different types such as rooms (Room1, Room2), per-sons (Person1, P erson2), personal computers (PC1, PC2),a smart phone (Phone), and a digital digital file (CV ). Thenesting of agents describes their locality indicating that, for ex-ample, the room on the left side in the model houses additionalagents, namely a person, a personal computer, and a smartphone. Vice versa, these agents are described to be located

6The knowledge base consists of a database and a rule base containing thescaling factors and functions as well as the fuzzy rule compositions [19].

in this particular room. The network of relationship amongagents, on the contrary, is exemplified by means of edgesrepresenting their interrelations. The given system involvesfour relationships illustrating a physical connection among thetwo rooms (door), a network link connecting both personalcomputers and the smart phone, a person operating a personalcomputer, and the same person possessing a file that is storedon one of the personal computers.

Room1 Room2

PC1

PC2

Person2

Person1

Phone

CV

Figure 3: Space

Bigraphs take this model of physical space as startingpoint and assign separate nodes to each agent. As arrangedin the physical space, the nesting of agents describes theirlocality, edges their linking. So called regions (dashed roundedrectangles) indicate independent sub-structures of agents thatare not part of others. The set of regions available in abigraph with its outgoing broken links defines its outer face.On the contrary, so-called sites describe the bigraph’s innerface as they represent placeholders for further bigraphs tobe inserted. The bigraph-based representation of the evolvingsystem described above is shown in Figure 4.

PC1

Person1

Phone

PC2

Person2

Room1

Room2

CV

0 1

Figure 4: Concrete Bigraph G

In order to be able to integrate information on both theagents’ placing as well as linking, bigraphs are composed oftwo independent graph structures on an identical set of nodes(e.g., N = {n0, n1, n2, n3}). On the one hand, a forest (set ofrooted trees7) describes the agents’ placing by depicting theirhierarchy. A hypergraph, on the other hand, describes theirnetwork of relationships. These structures are termed placegraph (GP ) and link graph (GL), forming together a bigraphG, so that

G = 〈GP , GL〉

7A rooted or oriented tree is a tree that has a designated root. Every nodeexcept the root has a unique parent.

A place-graph is a collection of rooted trees consolidated to aforest. Each tree depicts a region in the bigraph indicating anindependent sub-structure of agents that are not part of others.The place graph GP (cf. Figure 5) derived from the bigraphG is characterized by two rooted trees based on the regions{1, 2} serving as roots. All agents are subordinated to theseroots describing their hierarchical structure as it is determinedby their placing (nesting). The smart phone, for instance, issubordinated to the room where it resides which in turn isplaced in region 0. For the place graph GP , an outer face2 = {0, 1} and, as there are no sites, an empty inner face0 = {} is defined, resulting in

GP : 0 → 2

PC1

Room1

PhonePerson1 PC2

Room2

Person2

0 1

CV

Figure 5: Concrete Place Graph GP

A link-graph exhibits the same characteristics as an ordi-nary graph having nodes that are linked by edges. In contrastto an ordinary graph, however, a link graph allows edges toconnect any number of nodes8. In other words, a single edgee0 might not only connect two (e.g., N = {n0, n1}), but aset of nodes (e.g., N = {n0, n1, n2}). The link graph GL (cf.Figure 6) derived from the bigraph G describes the networkof relationships existent among agents.

A hyperedge connecting multiple nodes, for instance, isshown among the agents representing the personal computersand the smart phone. For the link graph GL, the outer as wellas inner face is defined as empty set ∅, resulting in:

GL : ∅ → ∅

PC1

Room1

PhonePerson1

PC2

Room2

Person2

CV

Figure 6: Concrete Link Graph GL

Subsuming the place as well as the link graph, the pairsas related to the inner and outer face are 〈0, ∅〉 and 〈2, ∅〉

8Graphs that allow edges (hyperedges) to connect any number of nodes arereferred to as hypergraphs.

respectively. The trivial inner face ε = 〈0, ∅〉 is the origin of abigraph, so that

G : ε→ 〈2, ∅〉

Up until now, the most fundamental basics were discussedby means of concrete bigraphs representing agents and theirnetwork of relationships (cf. Figure 4). Distinct roles, however,have not been assigned to edges so far. In doing so, agents areequipped with so called ports that define the roles of edgesthat are used to connect agents. Figure 7 gives a detailed viewon the bigraph G and shows an agent representing a personalcomputer. The black blobs where the edges impinge on theagent are ports defining the role as either of type ethernet orperson.

ethernetPC1

person

Figure 7: Ports

In further consequence, the concept of ports allows bi-graphs to be split and composed respectively, leading to somebigraphs that may serve as hosts for other bigraphs. Areaswhere other agents or even bigraphs as a whole are allowedto be inserted are defined by sites. Edges crossing these sitesindicate the bigraph’s inner face. An abstract bigraph as HostH provides flexibility in terms of concrete agents that are partof H at a particular point in time. Thus, H can host variousbigraphs satisfying its interface.

PC1

PhonePerson2

Room2

CV

Room1

0 1

0 1

ethernet

person

Figure 8: Abstract Bigraph H (Host)

Flexibility in order to be able to compose and decomposebigraphs is provided by the definition of abstract bigraphs. Thebehavior of evolving systems, however, cannot be representedso far as there is no possibility to describe the dynamics ofthese systems over time in terms of their structural evolution.For behavior specification, bigraphical reactive systems allowthe definition of reaction rules each consisting of two parts,a redex and reactum. The redex defines the bigraph to bechanged, whereas the reactum represents the changed bigraph.The delta between the redex and the reactum therefore de-scribes the set of changes in terms of the bigraph’s structurethat is required to be performed upon the system. A reaction

rule can be performed whenever the system’s bigraphicalstructure or parts of its structure conform to a rule’s redex.Upon execution, the system is transformed to the bigraphicalstate described by the corresponding rule’s reactum.

III. REPRESENTING INPUT SOURCES ANDDATA STREAMS

Bigraphs have been introduced based on the conceptsof space and motion. The input source configuration of anevolving intelligent system can be represented as a context-sensitive collection of data streams having a certain structureas well as behavior. On the one hand, the structure is depictedby the concept of connectivity where agents are interconnectedwith each other representing their interactions. Environmentalconditions, on the other hand, are addressed by the conceptof placing, meaning the nesting of agents. The behaviorand therefore the change of the composition of systems issupported by bigraphical reactive systems where a system’sstate is depicted before and after a transition.

In our approach, agents represent either evolving intelli-gent systems or the input source configuration in terms ofdifferent data streams including contextual conditions. Thesystem’s structure, behavior, and environment are addressedand described by the bigraphical characteristics of space andmotion. Consider a smart home control where an outdoortemperature sensor, a smart phone, and a barometer are usedin cooperation to control a room’s temperature by means of anHVAC system9. The smart phone, however, only influences thetemperature control when located in the respective room. Fromthe reverse perspective, the control system adapts its behaviorbased on the smart phone owner’s personal preferences whenin the room.

A. On-line Data Streams

Following this principle of representation, the smart homecontrol environment can be entirely represented by meansof bigraphical structures involving agents that describe therooms’ physical conditions (R1, R2), the temperature sensorT1, the barometer B, as well as the smart phone SP . Mutualdependencies are represented by means of edges describing thedata streams. The temperature sensor is interconnected with thesmart phone providing the data that is required for controllingthe temperature. In the context of bigraphical structures, thismapping can be represented as so-called link graph (cf. Figure9), being formally describe as FL : ∅ → ∅.

R1

B1

SPR2

light

temperature

temperature

T1

Figure 9: Smart Home Control FL (Concrete Link Graph)

9Heating, Ventilation, Air Conditioning

Systems with their input source configurations consideringcontextual conditions can be represented completely due tothe abstraction in the form of bigraphical structures. Repre-sentational flexibility is provided since the required level ofdetail can be adapted according to given requirements. In otherwords, systems are decomposed into sub-systems until therequired information can be made explicit in the form of agentsand their interconnections.

B. Contextual Conditions

In addition to the mapping of agents and their interrelationsby means of a link graph, the place graph FP depicts theplacing of agents that are involved in the smart home controlenvironment. In contrast to the original idea, the placing doesnot only describe an agent’s situation in terms of its physicallocation but also defines the environment in the sense ofits contextual surrounding influencing available data streams.The smart phone, for instance, provides data on the preferredtemperature but adapts its behavior according to its contextualsurrounding. As a consequence, the preferred temperaturemight decreases by 2 degrees once the smart phone moves fromroom number two to room number one. Figure 10 illustratesthe example’s place graph FP : 0 → 2 and shows the smartphone as well as the barometer that are subordinated to roomnumber 1. Room number 2 is represented as separate treestructure as it contains no agents.

B1

R1

0 1

R2T1

SP

Figure 10: Smart Home Control FP (Concrete Place Graph)

Based on the place graph FP and link graph FL (cf. Figure9 and 10), a bigraph F (cf. Figure 11) can be constructedincorporating both structures. The bigraph F , therefore, hastwo regions {0, 1} including the two rooms, the temperaturesensor, the smart phone, and the barometer. The bigraph doesneither exhibit any sites nor an inner or outer face as it isthe representation of the concrete control environment. Thebigraph F is represented on this basis as follows:

F : 〈FP , FL〉 ⇒ F : 〈0, ∅〉 → 〈2, ∅〉 ⇒ F : ε→ 〈2, ∅〉

C. Functional Behavior

The concrete bigraph F is a snapshot in time showingthe static structure of the control environment (cf. Figure 11).Neither the modification of the bigraph’s placing or linkingstructure nor the composition of bigraphs is possible. It doesnot allow any modifications during run time, such as the smartphone’s relocation. In order to add more flexibility in thismatter, the concrete bigraph F is required to be split up insome contextual bigraph FH serving as host (cf. Figure 12)and an inverse bigraph FP

10 to be inserted (cf. Figure 13).10The subscript P stands for post and refers to the bigraph to be inserted.

SP

T10 1R2

R1

B1

Figure 11: Smart Home Control F (Concrete Bigraph)

In the example, flexible parts are abstracted from the bigraphand replaced by some kind of placeholder – a site – allowingcomposition (cf. Figure 12). Broken links that emerge as aconsequence of abstraction are typecasted and make up thebigraph’s inner face {temp}. The place graph FP

H : 2 → 3,therefore, consists of one sites {0, 1} and two regions {0, 1, 2},whereas the link graph FL

H : {temp} → ∅ defines an innerface {temp} as well as an empty outer face ∅. Subsuming theplace as well as the link graph, the contextual bigraph FH isrepresented as follows:

FH : 〈FPH , F

LH〉 ⇒

FH : 〈1, {temp1, temp2}〉 → 〈2, ∅〉

temp2

0

B1

0 1T1R2

R1

temp1

Figure 12: Smart Home Control FH (Contextual Bigraph)

By means of representing the control system as abstractbigraph FH (the smart phone SP is replaced by a site), mod-ifications at run time are made possible through composition.In other words, FH allows including further bigraphs (e.g.,smart phones) as long as its inner face in terms of sites{0, 1} and set of broken links {temp1, temp2} is satisfied.Considering the example shown in Figure 11. The smartphone that was removed for composition purposes can now bedefined as separate bigraph satisfying the contextual bigraph’sinner face. In doing so, the smart phone is represented asindependent agent offering data streams of type temp1 andtemp2. Subsuming the bigraph’s place graph FP

P : 0 → 1 andlink graph FL

P : ∅ → {temp1, temp2}, FP is represented asfollows:

FP : ε→ 〈1, {temp1, temp2}〉

So far, the bigraphical structure represents the controlenvironment from the example as it is. Abstract bigraphswere derived to add more flexibility in terms of composition,however, do not make explicit structural changes that emergewhen, for instance, a smart phone appears to be available

SP0

temp 1

temp 2

Figure 13: Smart Phone FP

providing data. The specification of a bigraphical reactivesystem addresses this aspect and allows to encode function-ality by means of reaction rules, each composed of a redexand reactum respectively. Figure 14 illustrates an exemplaryreaction rule showing the bigraph’s structural modificationinvolved when a smart phone appears to be available but isnot located in one of the rooms yet. In order to be able tocomposed the two independent structures, their interfaces –inner and outer faces – need to match according to the definedtypecasts. In this case, the room requires some bigraph thatoffers a data stream providing data of type temperature. Oncea bigraph matches this pattern, the defined reaction rule canbe applied and the bigraph is transformed according to therule’s reactum. The smart phone then not only is connected toroom one but also moves its context. As a consequence, itsenvironmental conditions change that might exert influence onits behavior and provided data respectively. A site remainsto allow adding further components providing data for thepurpose of controlling.

1

SP

temp1

SP

T10 R1

B1

0 temp 2

0

0

temp2

B1

T1 R1

temp1

temp 2

Figure 14: Structural Adaptation (Reaction Rule)

IV. DYNAMIC INPUT SOURCE CONFIGURATION

The concept of bigraphs as foundation for representingevolving intelligent systems allows establishing a unified wayof interaction. The consistent depiction of these systems in-cluding their input source configurations in terms of avail-able data streams enables to match appropriate counterpartsdistributed across the networked universe of discourse. Thespecification of abstract bigraphs and their outer face and innerface respectively is sufficient in order to query other systemsand data streams as underlying technological and conceptualimplementations are considered from an abstract perspective.

A. Input Source Configuration

For illustration purposes, the bigraph-based input sourceconfiguration is shown by means of two agents representing asmart phone and a temperature sensor. Similar to the exampleabove, the smart phone is in charge of controlling the HVACsystem and for that reason needs to receive data from anexternal data stream. From a bigraphical point of view, the

smart phone might be represented as an agent offering twobroken links that represent the input data stream as well asthe outgoing temperature control (cf. Figure 15). Formally,this bigraph is described as SP : ε → 〈1, {temp1, temp2}〉.This representation allows the smart phone to be part of anyother contextual bigraph that satisfies its inner and outer face.That means that the smart phone can be applied to any otherenvironment which offers two links, namely for retrieving dataand controlling the temperature.

SP0

temp 1

temp 2

Figure 15: Smart Phone SP

As mentioned above, the smart phone requires some inputdata in order to control the connected agent’s temperature (e.g.,room). The bigraphical representation of a temperature sensoris depicted in Figure 16 that provides data in the specified for-mat. The temperature sensor by itself operates autonomouslybut is available to be connected to any other agent that is able toprocess its data stream in terms of temperature. In bigraphicalterms, the temperature sensor might be depicted as an agenthaving a broken link that represents its outgoing data stream.Two sites are available that facilitate the incorporation of otherbigraphs. By this means, not only further bigraphs providing(different) data streams but also bigraphs receiving this datacan be incorporated. Formally, this bigraph is described asfollows:

T : 〈2, {temp1, temp2}〉 → 〈2, ∅〉

10T1

0 1

temp 1

temp2

Figure 16: Data Stream (Temperature Sensor) T

Based on the set of broken links of both bigraphs (innerand outer face), the bigraph T representing the temperaturesensor can serve as host for the smart phone depicted in SP .Figure 17 illustrates the compound bigraph showing that thetemperature sensor is now directly connected to the smartphone. An additional site maintains the bigraph’s ability toincorporate further agents providing a broken link of typetemp1.

As a consequence to this approach, evolving intelligentsystems including their input source configurations are re-quired to either directly provide their data streams in theform of bigraphical structures or be connected to a third-party middleware (responsible for the corresponding transla-tion). In either case, interoperability is enabled by the levelof abstraction representing the respective components. Thematching of requests for interaction, as it was shown in the

0

T10 1

temp1

SP

temp 2

Figure 17: Dynamic Input Source Configuration

previous example, is addressed at this level. This matchinghowever is not limited to separate components as it concernsstructural adaptations represented by means of bigraphicalstructures (reaction rules). The matching of adaptations buildsupon the same principle but requires two abstract bigraphsthat describe the transition of an initial situation to an endstate. Consequently, intelligent systems cannot only be madeinteroperable but also are capable of evolving in terms ofadapting their structure of input source configuration.

B. Context-sensitive Handling of Input Sources

In the previous section, the ability of dynamic reconfigura-tion of evolving intelligent system was shown by means of bi-graphical structures. The interaction, however, was constrainedto the matching of needs of the involved intelligent systems.In other words, the dynamic arrangement has only concernedthe reconfiguration of these systems and their input sourcesbut has neglected their contextual conditions. In the domainof evolving intelligent systems (e.g., fuzzy systems), however,reconfiguration of input source might often comes along withcontextual changes that exert influence on the provided datastream. Consider a mobile HVAC controller (e.g., smart phone)that is required to adapt to spatial circumstances as one room(e.g., bedroom) needs to be colder than another (e.g., livingroom). When considering such contextual conditions whileachieving interoperability, bigraphs can not only be applied ascommon way to configure input sources but also to implementa respective middleware. Following up the previous example,the smart phone might not only consider incoming data forcontrolling the room’s temperature but might evolve and adaptitself to the data prevalent in its environment (e.g., room).Figure 18 describes the scenario and shows the smart phoneSP in the context of a room triggering the system to evolve.

Room1

SP

0

Figure 18: Contextual Representation of an Information Source

Bringing together the different aspects of bigraph-basedrepresentation of evolving intelligent systems, this approachnot only allows to achieve interoperability among componentsdistributed over the network but also facilitates to capture theircontextual conditions (cf. Figure 19). The temperature sensorT1, for instance, is spatially distant to the system S (smart

phone) that is using its outgoing data stream for controllingthe room’s temperature (R). The room’s agent, however, is notonly available to be connected to the controller but also deter-mines the contextual conditions relevant for its subordinatedagents. In the concrete case, the room’s contextual parametersmight influence the way of how the smart phone S behaves interms of temperature control.

SP

T1

1 R1

B1

0

Figure 19: Bigraph-based Evolving Intelligent System

C. Modeling Tool Support

The modeling of evolving intelligent systems includingtheir dynamic input source configuration has been discussed ona conceptual level in the previous sections. Modeling support,however, has not yet been addressed so far but is essentialwhen utilizing bigraph-based representation. Figure 20 shows aprototypical implementation of a modeling editor that enablesthe processing of such bigraphs in the context of evolvingintelligent systems. It provides full functionality to createand edit formally well-defined bigraphs with respect to theirstructure as well as behavior. In order to be flexible whilemodeling, a standardized protocol for transmitting bigraph-based data allows for dynamic treatment of change. Alterationsduring runtime, as for example the incorporation of newsystems or the availability of new data streams, are madeavailable instantly in the editor. Access to the most up to dateconfiguration in terms of systems and features available on thenetwork is therefore given (cf. Figure 20).

Figure 20: Bigraph-based Modeling Editor

This flexibility in orchestration not only supports the in-telligent systems to evolve but also allows these systems and

corresponding input sources as well as data streams to dynam-ically adapt to their contextual conditions. Modifications in thebigraph subsequently propagate right up to the system as wellas the user interface triggering respective adaptations. Consideran HVAC controller that dynamically detects and incorporatesavailable data streams and other evolving intelligent systemsfor the purpose of optimizing temperature control. An emerg-ing thermometer placed in a distant location therefore mightbe incorporated dynamically at run time allowing weatherforecasts for optimized pre-heating.

V. CONCLUSION

Self-evolving capabilities of (intelligent) systems are con-sidered evermore crucial in order to endure in today’s dynamicenvironments. The ability to structurally and parametricallyadapt to changing circumstances not only fosters system flex-ibility but also enables a system to retain a sufficient extent ofstability and plasticity over time. In this context, evolvabilityis not a term that solely concerns continuous development ofthe system itself, but rather describes a more comprehensiveconcept that includes the system’s surroundings in terms ofinput source configuration as well. For instance, a smart homemight make progress in terms of its reasoning model (e.g.,rules) but also needs to adapt to changing circumstancesconcerning the availability of input sources. This means that asystem not only is required to to adapt its behavior, but needsto reflect on how the configuration of external input sourceschanges as well.

The bigraph approach allows modeling input sources indynamic environments, namely through context-sensitive inputsource configuration. Input sources are represented by theirstructure, behavior, as well as contextual conditions usingbigraphical structures. The consistent depiction not only allowsfor inherent interoperability but also supports the orchestrationof different input sources that might even be distributed acrossthe network. Two modes of configuration can be establisheddue to this abstraction. First, user-driven configuration issupported by means of a modeling tool providing functionalityto manipulate bigraphs in real time. Due to the abstraction,users are capable of changing the systems input source con-figuration at run time without being cognitively and physicallyoverloaded. Secondly, the formal definition of bigraphs enablesself-controlled system configuration. This means that the or-chestration of input sources can be automatically generatedbased on prevailing circumstances. No user input is requiredas the system can derive composition rules autonomously.Since this approach requires a specialized infrastructure, abigraph-based middleware needs to be provided. A centralizedrepository needs to be in place that enables the configuration ofinput sources according to their contextual conditions. Futureresearch will continue to promote the development of such amodel repository.

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