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Abstract
Figures
Introduction
Restful Api Design
Conceptual Use Case
Related Work
Materials and Methods
Conclusions
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LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning

Profile image of Andrea OmiciniAndrea Omicini

Big Data and Cognitive Computing

https://doi.org/10.3390/BDCC2030023
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26 pages

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Abstract

In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in order to achieve key properties such as observability, explainability, and accountability. In this paper we focus on logic programming (LP), and advocate its role as a provider of symbolic reasoning capabilities in IoT scenarios, suitably complementing non-symbolic ones. In particular, we show how its re-interpretation in terms of LPaaS (Logic Programming as a Service) can work as an enabling technology for distributed situated intelligence. A possible example of hybrid reasoning—where symbolic and non-symbolic techniques fruitfully combine to produce intelligent behaviour—is presented, demonstrating how LPaaS could work in a smart energy grid...

Figures (10)
Figure 1. Logic Programming as a Service (LPaaS) contribution with respect to (i) Internet of Things (IoT) software engineering (SE) challenges and (ii) complementing non-symbolic approaches. _ As depicted in Figure 1, LPaaS makes symbolic approaches suitable for the loT domain by tackling the SE challenges which traditionally hindered its exploitation. In particular, the re-interpretation of the LP paradigm under the microservices architecture paves the way towards designing intelligence according to interoperable, reliable, composable, and scalable functionalities, while enabling observability and understandability. In the following section, we present the LPaaS model and architecture as well as the software engineering process adopted to deliver it.
Figure 1. Logic Programming as a Service (LPaaS) contribution with respect to (i) Internet of Things (IoT) software engineering (SE) challenges and (ii) complementing non-symbolic approaches. _ As depicted in Figure 1, LPaaS makes symbolic approaches suitable for the loT domain by tackling the SE challenges which traditionally hindered its exploitation. In particular, the re-interpretation of the LP paradigm under the microservices architecture paves the way towards designing intelligence according to interoperable, reliable, composable, and scalable functionalities, while enabling observability and understandability. In the following section, we present the LPaaS model and architecture as well as the software engineering process adopted to deliver it.
“ The service is initialised on the server at deployment-time. Once started, it can be accessed by clients (querying the KB for logic facts or asking for proofs of admissible goals), sources (meant 0 represent sensors and actuators willing to update the KB with latest measurements and action feedbacks), and configurators (privileged agents with the right to dynamically re-configure the service at run-time, subject to proper access credentials) via the corresponding interfaces. Since sources can modify the theory by adding new facts, they are designed as privileged clients (access credentials have to be provided). Specifically, the client interface exposes methods for observation and usage, the source interface provides methods for adding data (logic facts) to the KB, and the configurator interface accounts for service configuration, as depicted in Figure 2a. Figure 2b shows an example of use of the source interface. There, the LPaaS service has been deployed on a smart home conditioner to control the temperature of a room. The device autonomously decides when to turn itself on/off based on measurements of two sensors—namely, temperature and presence. Once the service is properly configured through the configurator interface (not depicted), the two sensors exploit the source interface to add up-to-date facts to the KB, while the user or other devices can access the service exploiting the client interface. Details on the interfaces’ APIs are discussed in Section 3.3 below. Figure 2. The LPaaS actors and their interfaces (a). An example of the use of the source interface (b)
“ The service is initialised on the server at deployment-time. Once started, it can be accessed by clients (querying the KB for logic facts or asking for proofs of admissible goals), sources (meant 0 represent sensors and actuators willing to update the KB with latest measurements and action feedbacks), and configurators (privileged agents with the right to dynamically re-configure the service at run-time, subject to proper access credentials) via the corresponding interfaces. Since sources can modify the theory by adding new facts, they are designed as privileged clients (access credentials have to be provided). Specifically, the client interface exposes methods for observation and usage, the source interface provides methods for adding data (logic facts) to the KB, and the configurator interface accounts for service configuration, as depicted in Figure 2a. Figure 2b shows an example of use of the source interface. There, the LPaaS service has been deployed on a smart home conditioner to control the temperature of a room. The device autonomously decides when to turn itself on/off based on measurements of two sensors—namely, temperature and presence. Once the service is properly configured through the configurator interface (not depicted), the two sensors exploit the source interface to add up-to-date facts to the KB, while the user or other devices can access the service exploiting the client interface. Details on the interfaces’ APIs are discussed in Section 3.3 below. Figure 2. The LPaaS actors and their interfaces (a). An example of the use of the source interface (b)
Table 1. LPaaS configurator interface (left) and LPaaS source interface (right). The LP service offers a set of methods to provide configuration and contextual information about he service and its KB, and a set of methods to query the service for triggering computations and reasoning, and to ask for solutions. In particular, the configurator interface makes it possible to set the service configuration, its KB, and the list of admissible goals. The source interface allows new facts o be added to the KB, meant to represent sensors and actuators with the latest measurements or feedback actions. The client interface makes it possible to query the service about its configuration parameters (stateful/stateless and static/dynamic), the state of the KB, and the admissible goals and facts. Moreover, the interface allows the service to be asked for one solution, N solutions, or all solutions available. These operations are slightly different in case of stateless or stateful requests. In the
Table 1. LPaaS configurator interface (left) and LPaaS source interface (right). The LP service offers a set of methods to provide configuration and contextual information about he service and its KB, and a set of methods to query the service for triggering computations and reasoning, and to ask for solutions. In particular, the configurator interface makes it possible to set the service configuration, its KB, and the list of admissible goals. The source interface allows new facts o be added to the KB, meant to represent sensors and actuators with the latest measurements or feedback actions. The client interface makes it possible to query the service about its configuration parameters (stateful/stateless and static/dynamic), the state of the KB, and the admissible goals and facts. Moreover, the interface allows the service to be asked for one solution, N solutions, or all solutions available. These operations are slightly different in case of stateless or stateful requests. In the
The table reports only methods operating on a dynamic KB that take an additional Timestamp argument, expressing the required time validity; static KB methods are analogous without the Timestamp parameter: for a full description of the API, we refer the reader to [47]. Table 2. LPaaS client interface.
The table reports only methods operating on a dynamic KB that take an additional Timestamp argument, expressing the required time validity; static KB methods are analogous without the Timestamp parameter: for a full description of the API, we refer the reader to [47]. Table 2. LPaaS client interface.
_ Mapping the logical architecture onto a concrete architecture offers some degrees of freedom. The choice here is to design the whole LPaaS microservice as a composition of three microservices—one for each logical unit. In this way, scalability and portability are extended beyond LPaaS inference service, offering a more fine-grained control on deployment—for instance, enabling the effortless relocation or replication of the whole LPaa&, of the sole inference engine (service), or of the KB alone The choice fits the IoT scenarios well where applications usually have a datastore—for instance, in the context of Big Data (IoT-Big Data), where data are continuously generated, mainly from sensor systems. In such scenarios, the above choice makes it easy to scale the KB microservice in/out individually. Such a modular decomposition also allows for easy and efficient deployment on a network of distributed devices hosting LPaaS.
_ Mapping the logical architecture onto a concrete architecture offers some degrees of freedom. The choice here is to design the whole LPaaS microservice as a composition of three microservices—one for each logical unit. In this way, scalability and portability are extended beyond LPaaS inference service, offering a more fine-grained control on deployment—for instance, enabling the effortless relocation or replication of the whole LPaa&, of the sole inference engine (service), or of the KB alone The choice fits the IoT scenarios well where applications usually have a datastore—for instance, in the context of Big Data (IoT-Big Data), where data are continuously generated, mainly from sensor systems. In such scenarios, the above choice makes it easy to scale the KB microservice in/out individually. Such a modular decomposition also allows for easy and efficient deployment on a network of distributed devices hosting LPaaS.
The three interfaces from Section 3.2 are collapsed within a unified RESTful API exposing the same resources to clients possibly playing different roles—namely client, source, or configurator. The two latter roles, since they are capable of applying side-effects to the service KB, require authentication. There are four main REST resource types provided: theories, goals, solutions, and authorisation tokens—as shown in Figure 4, along with their sub-resources and supported operations. All resources are accessible by specifying the so-called entry point after the service base URL, for instance, www.lpaas-example.it/ theories, where clients may exploit the logic service functionalities by issuing HTTP requests on the corresponding entry point. The response contains the requested information (if possible), an acknowledgement, or some link for late access to the result. For instance, users wanting to play some privileged role POST their credential to the/auth entry point. A comprehensive description of the RESTful entry points and their supported HTTP methods is provided in Table 3, in terms ui mapping the LPaaS interface API given in Tables 1 and. 2. sy 16 +: eomy. an =a ae: ye 4.1. RESTful API Design pe 2 eee ee ee ee ee ae ee Se ee a eae oe ee ee Es Meenre center Sewers ees Conn) Wee a ee Configurators can create theories by POSTing a well-formed Prolog theory on the /theories entry point. There are multiple named theories and, for each theory, many versions can be maintained According to time situatedness, each version of a theory is valid from the instant it has been POSTed (service local time) until either a newer version is POSTed, or some modification is made through some other entry point. The specific version, say the one valid at time t, of a particular theory n can be read by clients via a GET operation at the /theories/n/history/t entry point. In regard to spatial situatedness, whenever information—i.e., a theory or fact version—is retrieved via a GET operation, the response includes a spatial tag defining the position in space where the returned information can be considered valid.
The three interfaces from Section 3.2 are collapsed within a unified RESTful API exposing the same resources to clients possibly playing different roles—namely client, source, or configurator. The two latter roles, since they are capable of applying side-effects to the service KB, require authentication. There are four main REST resource types provided: theories, goals, solutions, and authorisation tokens—as shown in Figure 4, along with their sub-resources and supported operations. All resources are accessible by specifying the so-called entry point after the service base URL, for instance, www.lpaas-example.it/ theories, where clients may exploit the logic service functionalities by issuing HTTP requests on the corresponding entry point. The response contains the requested information (if possible), an acknowledgement, or some link for late access to the result. For instance, users wanting to play some privileged role POST their credential to the/auth entry point. A comprehensive description of the RESTful entry points and their supported HTTP methods is provided in Table 3, in terms ui mapping the LPaaS interface API given in Tables 1 and. 2. sy 16 +: eomy. an =a ae: ye 4.1. RESTful API Design pe 2 eee ee ee ee ee ae ee Se ee a eae oe ee ee Es Meenre center Sewers ees Conn) Wee a ee Configurators can create theories by POSTing a well-formed Prolog theory on the /theories entry point. There are multiple named theories and, for each theory, many versions can be maintained According to time situatedness, each version of a theory is valid from the instant it has been POSTed (service local time) until either a newer version is POSTed, or some modification is made through some other entry point. The specific version, say the one valid at time t, of a particular theory n can be read by clients via a GET operation at the /theories/n/history/t entry point. In regard to spatial situatedness, whenever information—i.e., a theory or fact version—is retrieved via a GET operation, the response includes a spatial tag defining the position in space where the returned information can be considered valid.
Table 3. The Respresentational State Transfer (REST)ful reification of LPaaS API.
Table 3. The Respresentational State Transfer (REST)ful reification of LPaaS API.
Figure 5. The LPaaS development process. ete | CLI bt AGY OLCLVILe. In particular, the distributed software development [49] relies on the GitLab source code repository and Git (https://git-scm.com/) as a versioning tool. Build cycles are handled through the Gradle build system(https://gradle.org/) (migrating from Maven), while regression/integration testing amongst inner microservices exploits GitLab’s CI/CD Pipelines feature (https: //docs.gitlab.com/ ee/ci/pipelines.html). Finally, the deployment on the Docker platform (https: //www.docker.com/ what-docker) relies on integration with the GitLab pipeline through the Docker Engine feature (https: //docs.gitlab.com/ce/ci/docker/using_docker_build.html).
Figure 5. The LPaaS development process. ete | CLI bt AGY OLCLVILe. In particular, the distributed software development [49] relies on the GitLab source code repository and Git (https://git-scm.com/) as a versioning tool. Build cycles are handled through the Gradle build system(https://gradle.org/) (migrating from Maven), while regression/integration testing amongst inner microservices exploits GitLab’s CI/CD Pipelines feature (https: //docs.gitlab.com/ ee/ci/pipelines.html). Finally, the deployment on the Docker platform (https: //www.docker.com/ what-docker) relies on integration with the GitLab pipeline through the Docker Engine feature (https: //docs.gitlab.com/ce/ci/docker/using_docker_build.html).
Figure 6. The LPaaS smart energy application scenario. 5.1. The State of Art
Figure 6. The LPaaS smart energy application scenario. 5.1. The State of Art
Figure 7. The LPaaS energy management in the smart house. | ne aed: Danes: : nea * inion: ene (eee ile Also, we would like to emphasise that all of the above is made possible by the re-interpretation of distributed logic programming provided by the LPaaS model and technology, which, above all, is meant to promote and support the distribution of micro-intelligence wherever needed. A similar scenario would not be possible with traditional logic programming, where there is no concept of situatedness in space and time, and where only a single monolithic theory of the whole world is considered. Related works, sources of inspiration for the re-interpretation of logic programming, and for the design of LPaaS are discussed in next section. 6. Related Work
Figure 7. The LPaaS energy management in the smart house. | ne aed: Danes: : nea * inion: ene (eee ile Also, we would like to emphasise that all of the above is made possible by the re-interpretation of distributed logic programming provided by the LPaaS model and technology, which, above all, is meant to promote and support the distribution of micro-intelligence wherever needed. A similar scenario would not be possible with traditional logic programming, where there is no concept of situatedness in space and time, and where only a single monolithic theory of the whole world is considered. Related works, sources of inspiration for the re-interpretation of logic programming, and for the design of LPaaS are discussed in next section. 6. Related Work

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