The ontology for agents,systems and integration of services: OASIS version 2 $

1 Introduction

The gist of the Semantic Web [1] is to achieve the full interoperability of software applications by means of reusing and sharing of data in standard formats across systems, enterprises, and community boundaries. In the Semantic Web vision of the Internet, software agents are enabled to query and manipulate information on behalf of human agents by means of machine-readable data. Such data carry explicit meaning, expressed with formally defined semantics through suitable representation languages that support the automatic processing of information. For this purpose, the Word Wide Web Consortium (W3C) conceived the Web Ontology Language (OWL) [2], a family of knowledge representation languages relying on Description Logics [3], as a standard for representing Semantic Web ontologies. 1 The main advantage of Semantic Web is to enable users and applications to access data-oriented web content: users and applications become able to discover and invoke web resources in a completely automatic way. To capture the complex interactions of the participants in those environments where people and organizations dynamically interact with autonomous systems, it is convenient to identify the proactive stakeholders as autonomous agents [4]. Agents are defined as entities whose state is perceived as a set of mental components such as beliefs, capabilities, choices, and commitments [5]. This definition gives rise to the foundations for the Agent Oriented Programming (AOP) paradigm, which is most often motivated by the need for open architectures that continuously evolve to adapt to external changes in a robust, autonomous, and proactive way. Coherently with this vision, defining suitable representation mechanisms for agents is still challenging in the large realm of Knowledge Representation.

The authors’ choice for the semantic description of agents focuses on the agents’ mental states related with their capabilities of reaching an achievement. Then, the behaviouristic approach, inspired by the Tropos methodology [6], provides a practical and complete operational semantics. Agent behaviours are decomposed in the atomic and essential mental states of the agent, in such a way that each single task that the agent seeks to accomplish is completely described. The Ontology for Agents, Systems and Integration of Services [7, 8], OASIS in short, applies the behaviouristic approach to deliver a representation system and a communication protocol for agents and their commitments. Agents are enabled to manifest the set of operations that they are able to perform and the type of data required to execute them. Each output is also identified in a clear, human-understandable and unambiguous way, in order to abstract from the implementation details and transparently automate the task of agent discovery. Agents may then join a collaborative environment in a plug-and-play way, since third-party interventions are no longer required, thereby profiting from the Semantic Web features. By means of Semantic Web technologies and of automated reasoners, data provides machine-understandable information that can be processed, integrated and exchanged by any type of agent, at a higher level; data consistency can be easily verified, and information can be inferred and retrieved through the defined knowledge base. This leads to the realization of Smart Agents, IA-driven agents that consume information and act with minimal human intervention. Where required, agents are able to communicate with humans by using common natural interaction practises, like vocal commands, sentences transmitted via texts or gestures.

This article revises a previous workshop paper [8] by reporting on the full modelling choices behind the latest version of OASIS , named OASIS 2. In brief, the new ontology reviews the representation of agents and their commitments and also introduces the constitutional elements that make it a valid means for defining a Semantic Blockchain. The full OASIS 2 is available online [9].

The paper is organized as follows. Section 2 introduces the main features of OASIS 2 and how it models agent behaviours and commitments. Section 3 reports on the contributions given by OASIS , depicting the main differences between OASIS and OASIS 2. Section 4 presents related works discussing similarities and differences with OASIS 2, while Section 5 concludes the paper with future perspectives.

2 The OASIS 2 ontology

This section is devoted to the OASIS 2 modelling approach for representing agents and their commitments. OASIS 2 is a foundational OWL 2 ontology that leverages the behaviouristic approach derived from the Theory of Agents and the related mentalistic notions to represent multi-agent systems. The approach aims at describing how agents behave with respect to the environment by representing their essential mental states, namely behaviours, goals, and tasks. Behaviours represent the mental state of the agent associated with its capability of activating, modifying the environment or doing something; goals describe mental attitudes representing preferred progressions of a particular system that the agent has chosen to put effort into bringing about [10]; tasks depict how such progressions are carried on and describe atomic operations that agents may perform.

Such behaviouristic approach is an effective way for semantically describing agents by characterizing their capabilities. Agents are enabled to report the set of activities that they are able to perform, the types of data required to execute those activities as well as the expected output through the description of their behaviours. Agents’ implementation details are abstracted away so as to make the discovery of agents transparent, automatic, and independent of the underlying technical infrastructure. As a consequence, agent commitments are clearly described and the entire evolution of the environment is unambiguously represented, searchable, and accessible: agents may join a collaborative environment in a plug-and-play fashion, as there is no more need of third-party interventions. The behaviouristic approach is exploited by OASIS 2 to characterize agents in terms of the actions they are able to perform, including purposes, goals, responsibilities, information about the world they observe and maintain, and their internal and external interactions. Finally, OASIS 2 models the executions and assignments of tasks, restrictions, and constraints used to establish agent responsibilities and authorizations. In OASIS 2, agents and their interactions are represented by carrying out three main steps, namely a) an optional step that consists in modelling general abstract behaviours (called templates) from which concrete agent behaviours are drawn, b) modelling concrete agent behaviours, possibly, drawn by agent templates, and c) modelling actions and associating them with the corresponding behaviours.

The first step, not mandatory, consists in defining the agent’s behaviour template, namely a higher-level description of the behaviour of abstract agents that can be implemented to define more specific and concrete behaviours of real agents; for example, a template may be designed to describe which features a trader agent should own. Moreover, templates are useful to guide developers in the definition of the behaviours of their specific agents. To describe abstract agent’s capabilities to perform actions, an agent template comprises three main elements, namely behaviour, goal, and task. In fact, the core of the agent representation mechanism in OASIS 2 revolves around the description of atomic operations introduced by the definition of tasks, i.e., the most simple (atomic) operations that agents are able to actually perform including, possibly, input and output parameters required to accomplish them.

The second step consists in representing concrete agent behaviours either by specifying a shared template or by defining it from scratch. In both cases, concrete behaviours are modeled analogously to those of templates, where the models of ideal features are replaced with actual characteristics. Behaviours drawn by shared templates are clearly associated with them in order to depict the behaviour inheritance relationship.

In the last step, actions performed by agents are described as direct consequences of some behaviours, and are associated with the behaviours of the agent that performed them. To describe such an association, OASIS 2 introduces plan executions. Plan executions describe the actions performed by an agent and associated with one of its behaviours. Associations are carried out by connecting the description of the performed action to the behaviour from which the action has been drawn: actions are hence described by suitable graphs that retrace the model of the agent’s behaviour. Additionally, plans can be either submitted to agents as request of performing some actions or they can be assigned by specific agents called entruster agents.

In OASIS 2, agent templates are defined according to the UML diagram in Fig. 1.

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Fig. 1

UML diagram of agent templates in OASIS 2.

To describe how both abstract and concrete agents perform actions, the description of agents comprises three main elements, namely behaviour, goal, and task. Agent tasks, in their turn, describe atomic operations that agents may perform, including possibly input and output parameters required to accomplish them. Those elements in OASIS 2 are introduced by way of the following classes:

Agent: this class comprises all the individuals representing agents. Instances of such a class are connected with one or more instances of the class Behaviour by means of the OWL object-property hasBehaviour.

The core of agent behaviour revolves around the description of atomic operations represented by instances of the class TaskDescription that characterizes the mental state corresponding to commitments. In their turn, instances of the class TaskDescription are related with the following five elements that identify the operation:

An instance of the class TaskOperator, characterizing the mental state corresponding to the action to be performed. Instances of TaskOperator are connected either by means of the object-property refersExactlyTo or refersAsNewTo to instances of the class Action. The latter class describes physical actions represented by means of entity names in the form of infinite verbs (e.g., produce, sell). Specifically, the object-property refersExactlyTo is used to connect the task operator with a precise action having a specific IRI, whereas refersAsNewTo is used to connect a task operator with an entity representing an action of which only a general abstract description is given (for example, an action for which only the type is known).

In the latter case, the action is also defined as an instance of the TemplateThing: such instances are used to introduce entities that represent templates for the referred element describing the characteristics that it should satisfy. To specify the classes that the entity must be instances of, it is possible to connect the entity with instances of the desired classes using the object-property refersAsInstanceOf. Finally, TemplateThing is a novel OASIS 2 class, used to characterize all the individuals involved in the definition of behaviour templates and to distinguish them from the entities representing concrete behaviours, plans or actions, thus eliminating the need of having separated models for those aspects.

Finally, in the case of agent behaviour templates, instances of Agent, Behaviour, GoalDescription, TaskDescription, TaskOperator, TaskOperatorArgument, TaskObject TaskInputParameter, and TaskOutputParameter are also instances of TemplateThing.

Fig. 2 illustrates an example of an agent template describing the procedure of smart contracts for minting ERC-721 compliant non-fungible tokens (NFTs) on the Ethereum blockchain. The agent template described in the example comprises a single behaviour, constituted by a single goal that in its turn comprises a single task. The task, which represents the ability of the agent to mint a NFT, provides four elements: OPEN IN VIEWER

In the second step, concrete agent behaviours are defined either by instantiating one or more templates or from scratch. In OASIS 2, the modelling pattern of concrete behaviours has a structure analogous to the one of behaviour templates, illustrated above, with the difference that individuals used to define a concrete behaviour are instances of the class BehaviourThing (instead of instances of the class TemplateThing).

An example of concrete behaviour is illustrated in Fig. 3. 2 The figure depicts a concrete behaviour for minting ERC-721 tokens, devised from the template in Fig. 2. The template is implemented by the concrete behaviour that provides a graph similar to the template’s one, where the instances of TemplateThing are replaced with instances of BehaviourThing. The entity connected with the task object and the output parameter remain the same both in the template and in the concrete behaviour so as to establish that recipients of the concrete behaviours are objects of type EthereumTokenERC721 or of one of its subclasses.

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Fig. 3

Example of OASIS 2 concrete behaviour.

Concrete behaviours can be connected with the template they are drawn from. In order to describe the fact that concrete agents inherit their behaviours from a common shared template, the instances related with the concrete behaviours are connected with the instances of the template through the sub-properties of the object-property overloads. The association is carried out by connecting the instances of the classes:

Behaviour, by means of overloadsBehaviour;

As a last step, agent commitments devised from behaviours are introduced to describe agent actions. In OASIS 2, commitments are represented by adopting the same pattern presented for abstract behaviours with the difference that i) instances of the class TemplateThing are instead modeled as instances of the class ExecutionThing and ii) the agent responsible for the execution of the action is related with the plan representing the commitment by means of the object-property performsPlanExecution, subproperty of performs. The class ExecutionThing is introduced to characterize all the entities involved in the definition of concrete actions and to distinguish them from the ones introduced for templates, behaviours, and plans. For instance, Fig. 4 depicts the action of minting the token number 32 committed by the smart contract whose behaviour is illustrated in Fig. 3. In the example, suitable instances of ExecutionThing that represent the commitment stages take the place of the instances of BehaviourThing, introduced in the concrete behaviour. Moreover, the template representing general Ethereum tokens has been replaced with an instance representing the token number 32, namely SWB_token32.

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Fig. 4

Example of OASIS 2 agent’s commitment.

In order to relate agent commitments with the behaviour from which they are drawn, subproperties of the object-property drawnBy are introduced. Specifically, planExecutionDrawnBy connects the instance of GoalDescription of the agent action to its analogue of agent behaviour; much in the same way, goalExecutionDrawnBy connects the instance of the class GoalDescription of the commitment with its analogue, while taskExecutionDrawnBy, taskObjectDrawnBy, taskOperatorDrawnBy, taskInputParameterDrawnBy, and taskOutputParameterDrawnBy are introduced for TaskDescription, TaskObject, TaskOperator, TaskInputParameter, and TaskOutputParameter, respectively.

Agents that desire to engage peers with performing actions can submit plans, namely descriptions of actions that they wish to be realized. For plans, a model analogous to the one describing behaviours have been introduced, where instances of BehaviourThing are replaced with instances of PlanningThing and agents proposing plans are connected with the instances of PlanningThing and Behaviour of the plan by means of the object-property requestsPlan (see Fig. 5). The class PlanningThing is introduced to characterise all the individuals involved in the definition of plans and to distinguish them from the ones introduced for representing templates, behaviours, and actions.

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Fig. 5

UML diagram of agent plans in OASIS 2.

Usually, agents proposing plans identify the behaviours responsible for their realization beforehand, in such a way as to completely describe and trace how agent intentions are realized. In this case, the entities representing the submitted plan are related with the entities describing the responsible behaviour by means of suitable subproperties of the object-property submittedTo, relating instances of PlanningThing with instances of BehaviourThing as follows: a) planDescriptionSubmittedTo, for instances of Behaviour; b) goalDescriptionSubmittedTo, for instances of GoalDescription; c) taskDescriptionSubmittedTo, for instances of TaskDescription, d) taskObjectSubmittedTo, for instances of TaskObject; e) taskOperatorSubmittedTo, for instances of TaskOperator; f) taskInputParameterSubmittedTo, for instances of TaskInputParameter; and g) taskOutputParameterSubmittedTo, for instances of TaskOutputParameter.

In a similar way, plans are also related with the agent’s action realizing them. For this purpose, the subproperties of the object-property hasExecution are introduced, namely hasPlanExecution, hasGoalExecution, hasTaskExecution, hasTaskObjectExecution, hasTaskOperatorExecution, hasTaskInputParameterExecution, and hasTaskOutputParameterExecution.

In OASIS 2, agents can assign plans to agent peers that can perform them by means of entrustments. Entrustments represent the capability of entruster agents to engage a performer agent so that it executes a given plan submitted by a requester agent (for example, a domotic assistant that activates an IoT device on behalf of a human agent). The association among the behaviours of entruster, requester, and performer are illustrated in Fig. 6. The entrustment is introduced by means of a graph that retraces the structure of that of agent behaviours, where instances of BehaviourThing are replaced with instances of EntrustmentThing. In order to associate a plan with the behaviour of the agent chosen to perform it, the elements of the entrustment are connected to a) the related elements of the plan, by means of the subproperties of entrustedBy, and to b) the related elements of the behaviour of the performer agent, by means of the subproperties of entrustedFrom. More precisely, entrustedBy provides the object-properties a) planEntrustedBy, to connect the entrustment to the requested plan; b) goalEntrustedBy, to connect the entrustment’s goal to the plan’s goal; c) taskEntrustedBy, to connect the entrustment’s task to the plan’s task; d) taskObjectEntrustedBy, to connect the entrustment’s task object to the plan’s task object; e) taskOperatorEntrustedBy, to connect the entrustment’s task operator to the plan’s task operator; f) taskOperatorArgumentEntrustedBy, to connect the entrustment’s task operator argument to the plan’s task operator argument; g) taskInputParameterEntrustedBy, to connect the entrustment’s task input parameter to the plan’s input parameter; and h) taskOutputParameterEntrustedBy, to connect the entrustment’s task output parameter to the plan’s task output parameter. On the other hand, entrustedFrom provides analogous subproperties to connect each element of the entrustment to the related ones of the performer agents.

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Fig. 6

UML diagram of agent entrustments in OASIS 2.

A single plan entrustment may be constituted by more than one goal entrustment, which in its turn may be constituted by one or more task entrustments, depending on how the requested plan is structured. In case that the requested plan is constituted by two or more tasks, each task can be entrusted to different agent behaviours, thus ensuring the representation of cooperative systems, where the effort of solving plans is distributed among participants. Finally, when the performer agent executes the entrusted plan, a plan execution is introduced as described above. As a consequence, the plan entrustment is associated with the plan execution by connecting the elements of the plan entrustment to the related elements of the plan execution through the subproperties of entrustedWith in an analogous way to entrustedBy and entrustedFrom. Additionally, the task of the plan entrustment is associated with the performer agent by means of the object-property entrusts.

The first version of OASIS was presented in [7], together with the architecture design and implementation of a domotic assistant based on it, called PROF-ONTO. 3 Our approach represents a first foundational contribution to the adoption of Semantic Web technologies for defining a transparent communication protocol among agents. The proposed protocol was based on the exchange of RDF-based fragments of OASIS , each consisting of a description of a request to be fulfilled, by means of suitably constructed queries, against the description of the agent’s behaviour selected to satisfy it.

In [12], the OASIS ontology is extended with Ontological Smart Contracts (in short, OSCs) and conditionals. OSCs are intended as agreements among agents expressed through suitable ontological fragments that permit to establish responsibilities and authorizations among agents. Conditionals have many purposes, namely they are used a) to restrict and limit agent interactions, b) to define activation mechanisms that trigger agent actions, and c) to define constraints and contract terms on OSCs.

Additionally, OSCs can be secured through digital decentralized public ledgers such as the blockchain. For this purpose, [12] also sketches the architecture of a framework based on the Ethereum blockchain and the Interplanetary File System. The framework leverages a suitable Ethereum smart contract to claim and transfer non-fungible tokens (NFT) compliant with the Ethereum ERC-721 standard, representing the ownership of OWL ontological fragments stored on the IPFS. Moreover, smart contracts allow one to verify unauthorized modifications both on the hierarchy of imported ontologies and on the SPARQL queries defined to verify the OSCs. The work also represents a first approach towards the definition of a Semantic Blockchain, where operations over smart contracts are semantically represented. For this purpose, in [13], the definition of digital contracts is extended over the blockchain to include smart contracts, intended as programs running on the blockchain and interpreted as digital agents in an OASIS fashion, including their operational semantics and tokens exchanged through them. It also advances the first approach to formalize ERC-721 compliant smart contracts through Semantic Web ontologies by leveraging, in turn, the behaviouristic approach pursued by OASIS . The main capabilities of ERC-721 compliant smart contracts are represented and exploited to fully enable a Semantic Blockchain that provides agents and humans with means for probing the Ethereum blockchain, thus discovering smart contracts and their interactions, transactions, exchanges of cryptocurrencies and tokens, on the one hand, and for building oracles for distributed ledgers, on the other. The proposed approach is part of the POC4COMMERCE project [14] funded by the NGI-ONTOCHAIN consortium [15]. POC4COMMERCE, which leverages OASIS as foundational ontology, is presented in [16], while its design and analysis can be found in [17] and in [18]. In the context of the POC4COMMERCE project, a semantic search engine harnessing the expressive power of OASIS for the e-commerce realm is under development, currently standing at TRL3 (Technology Readiness Level 3).

Recently, an extension of OASIS towards OASIS 2 as described below has been presented in [8].

OASIS 2 enhances OASIS thanks to a substantial refactoring to re-shape behaviours, plans and actions. In OASIS , behaviour templates, behaviours, plans, and actions are introduced by four distinct graphs rooted in the classes BehaviourTemplate, Behaviour, Plan and PlanExecution, respectively (see [7] for the UMLs). Instead, OASIS 2 introduces the novel classes TemplateThing, BehaviourThing, PlanningThing, and ExecutionThing to characterize the individuals defining behaviour templates, behaviours, plans, and actions, respectively. For the four mentioned aspects of agents, OASIS 2 adopts the same graph involving the classes Behaviour, GoalDescription, and TaskDescription. As a consequence, the classes TaskFormalInputParameter and TaskFormalOutputParameter used to define parameters of behaviours in OASIS are replaced by the classes TaskInputParameter and TaskOutputParameter, adopted in combination with the class BehaviourThing, while the classes TaskActualInputParameter and TaskActualOutputParameter used to define parameters of agent actions are replaced by the classes TaskInputParameter and TaskOutputParameter, used in combination with the class ExecutionThing. Concerning agent templates, the input and output parameters are introduced by way of the classes TaskInputParameter and TaskOutputParameter, respectively, in combination with the class TemplateThing. Finally, the object-properties connecting the agent mental states (namely, consistsOfGoalDescription, consistsOfTaskDescription, hasTaskInputParameter and hasTaskOutputParameter) have been unified, thus removing the idiosyncrasy resulting from representing strictly correlated aspects (i.e., abstract behaviour, concrete behaviour, action, and plan) through distinct models.

Additionally, the new approach proposed by OASIS 2 streamlines the representation of agents, also clarifying their relationships and interactions, thus bringing coherence to the model and making it more compact, smooth, and consistent. Incidentally, the novel model supports the definition of more efficient and simple queries. Most importantly, OASIS 2 introduces the assignment of plans and, at the same time, models the constitutional elements of blockchains, which were missing in OASIS .

4 Related Work

The integration of agent systems and the Semantic Web has been investigated in several contexts [19–21] and the advantages of ontology-based applications have been recognized in the realm of MAS [22]. Ontologies for MAS have been modeled by taking approaches similar to those of Agent-Oriented Software Engineering (AOSE) [23], a software engineering paradigm for the development of complex MAS based on the abstraction of agent roles and on their organizations. In [24], an ontology for agent-oriented software engineering is proposed together with a tool that uses the ontology to generate programming code for MAS. The two approaches do not examine agents and their interactions in detail and do not take into account an appropriate modelling of agent communication, which, by contrast OASIS does since its inception thanks to a protocol based on the exchange of suitable RDF fragments.

Some results attempt to bring uniformity and coherence to the increasing volume and diversity of information in a specific domain, but domain-legacy generally makes them not applicable to other contexts even if they provide interesting insights for a general approach. The downside of those models lies in the absence of relationships between agents and their commitments, which instead represents the core of agent-oriented representations. That implies the inability of finding agents/services with specific capabilities, invoking them, and enabling their interoperability, which, by contrast is smooth in OASIS .

The authors of [25] propose an ontology-based framework for seamlessly integrating agents and Semantic Web Services, focusing on biomedical information, while an infrastructure to allow agent-oriented platforms to access and query domain-specific OWL ontologies is presented in [26]. An approach to design scalable and flexible agent-based manufacturing systems integrating automated planning with multi-agent oriented programming for the Web of Things (WoT) is introduced in [27]. Concerning the Internet of Things (IoT), ontological approaches mainly focus on the description of sensors, with the purpose of collecting data associated with them for generating perceptions and abstractions of the observed world [28]. A comprehensive ontology for representing IoT services is presented in [29] together with a discussion on how it can be used to support tasks such as service discovery, testing, and dynamic composition, taking into account also parameters such as Quality of Services (QoS), Quality of Information (QoI), and IoT service tests. A unified semantic knowledge base for the Internet of Things (IoT) is proposed in [30], capturing the complete dynamics of IoT entities, as for instance enabling semantic searching while hiding their heterogeneity. All these works are valuable and have influenced the gestation of OASIS . In consequence, our ontology is most general, offering an extensive range of constitutional elements to represent virtually any domain that is amenable to a behaviouristic formalisation, including web services, Web of Things (WoT), IoT, and quality evaluation.

In the realm of WoT, the W3C advances a formal model and a common representation for WoT descriptions based on a small vocabulary that makes it possible both to integrate diverse devices and to allow diverse applications to interoperate [31]. The representation system provides a way to expose the state of an object and to invoke functions that, however, must be known in advance. This may complicate the task of invoking agents that would like to join the environment in a plug-and-play manner. Moreover, the provided schema does not fully allow agents to interact according to the specific roles they aim to play. This is clearly possible in OASIS.

Worthy to be mentioned, conjoining the blockchain with ontologies [32, 33] is a recent research area, mainly focused in developing a characterization of blockchain concepts and technologies through Semantic Web but without a precise analysis of the operational semantics of smart-contracts. In [34], a theoretical contribution looking at the blockchain through an ontological approach has been provided. In [35], the authors propose a blockchain framework for SWoT contexts settled as a Service-Oriented Architecture (SOA), where nodes can exploit smart contracts for registration, discovery, and selection of annotated services and resources. Other works aim at representing ontologies within a blockchain context. For instance, in [36] ontologies are used as a common data format for blockchain-based applications such as the proposed provenance traceability ontology, but are limited to describe implementation aspects of the blockchain. Therefore, the semantic description of smart contracts and their actions is missing from the literature, and this is one of the major results brought by OASIS through its formalisation of smart contracts conceived as agents modeled according to the approach pursued by the behaviouristic vision.

5 Conclusions

This paper presented the epistemological choices of the latest release, OASIS 2, of Ontology for Agents, Systems and Integration of Services. OASIS 2 is a foundational ontology that takes the behaviouristic approach from the Theory of Agents and inherits the related mentalistic notions to represent agents. The paper focused on how OASIS 2 represents agent templates, concrete agents, agent commitments, plans, and entrustments (underlining the differences with respect to the previous version). It also introduced the representation of plan entrustments, a new feature of OASIS 2, and illustrated the main contributions of OASIS 2 and how it evolved since its first release, in particular as a consequence of its adoption in the realm of ontologies for blockchains. In fact, the NGI-ONTOCHAIN consortium aimed at delivering the ontological foundation of a blockchain-oriented e-commerce by adopting OASIS 2 and its behaviouristic approach.

Many advancements of OASIS 2 are in progress. Representing how agents reach consensus is one of the future challenges, together with the modelling of their behaviours. We shall also consider how to model in OASIS 2 agent roles, modular behaviours, and processes. In addition, we intend to apply agent conditionals to represent security constraints for cybersecurity threat contexts, in particular for the purpose of semantically representing authentication and confidentiality properties for agents. We shall consider how to integrate OASIS 2 with the main frameworks such as JADE [37] to automatically generate agents and artifacts, and how it can be exploited by CArtAgO [38], a framework for building shared computational worlds.