Automated context aware composition of Advanced Telecom Services for environmental early warnings

Highlights

• We present a Framework for automated composition of Advanced Telecom Services.

• We describe a metamodel for user context information.

• We translate requests in Natural language into automated planning instances.

Abstract

This paper presents one of the main components of a framework for automated composition of Advanced Telecom Services for environmental early Warnings. The framework, called AUTO, is composed by three main modules: a request processing module that transforms natural language and context information into a planning instance; the automated planning module, based on PELEA, an architecture for planning and execution; and the Service Execution Environment Advance Telecom Services. This paper focuses on the description of the translation of the user request in natural language and his context into planning instances. These planning instances represent service composition tasks based on Automated Planning. The advantages of this approach, like the automatic inclusion of context and user preferences in the composition of services, will be presented. Also, the current implementation will be described and some experimentation will prove the viability of AUTO.

Introduction

An Advanced Telecom Service is defined as a service that exploits the convergence of communication networks and takes also advantage of features accessible from the World Wide Web (Object Management Group [OMG], 2012). This kind of composite services are needed when the request of a client cannot be satisfied by a single service. Traditionally, the composition of Telecom Services in the industry is carried out through interactive graphical interfaces called Services Creation Environments (SCE),¹ which allow the user to articulate services functionalities using drag-and-drop tools. This approach is usually valid in Telecommunications thanks to the fact that the composition is performed using exclusively Telecom services, whose reliability is very high (99.999%) (Chrighton, Long, & Page, 2007). However, composing Advanced Telecom Services including services from the Internet, commonly called Web Services, is a very different matter. Due to the dynamic nature of the Internet, Web Services may change, become unavailable and grow in number to unmanageable sizes. This means that composition of advance telecom services is unfeasible in practice if the user employs traditional methods.

Previous works from both the academia (Hatzi et al., 2012; Hoffmann, Bertoli, & Pistore, 2007) and industry² have revolved around this topic. However, few academic approaches include implementations in real world scenarios, and few works from the industry are publicly open and easy to extrapolate to similar cases, as exposed by Dustdar and Schreiner (2005) Furthermore, previous work in the area focuses on representing the services using semantic descriptors (Huang, Lee, & Crespi, 2012) and applying afterwards different techniques, such as automated planning (Oh, Lee, & Kumara, 2006) or genetic algorithms (Ye, Zhou, & Bouguettaya, 2011). Additionally, some of these approaches have obtained promising results using Web Services, but no prior work that we are aware of is able to combine them with Telecom features. Another important limitation of these approaches is that they require that the users express their requests using formal languages such as Golog (Sohrabi, Prokoshyna, & McIlraith, 2009) or PDDL (Hatzi et al., 2012). This means that the interaction of a large number of people with little or no technical background with the system is unfeasible, which renders these works useful only in a limited set of scenarios.

The goal of the environmental early warning field is to create contingency plans that help resolve potentially harmful or dangerous situations, such as floods or tropical storms. The detection of these situations combines both information gathered from sensors around the monitored area and the input of relevant users. An example of such a situation would be evacuating all the villages close to a river after detecting that its level has risen beyond regular measurements or upon the request of an observer. In this case a contingency plan would include actions to monitor the river, determine affected areas, warn the villagers and coordinate the logistics of the evacuation. Since the participation of a human is required in critical scenarios, we assume that all the requests are triggered by users. Besides, in rural environments the access to a device able to send a request in the format specified by the system may be limited. This means that the system should be able to process natural language so it can be initiated through simple communication means like a phone call or an SMS. In fact, thanks to the rather restrictive process of the composition of services in this domain, the main procedures and their associated services can be described using simple semantic annotations by experts in the field, which allow a natural language recognition technique to be implemented without imposing significant restrictions to the potential users. This also means that an automatic translation of the input into a formal language without intervention from either the user or an expert is a much easier task.

Recent studies have explored the application of Natural Language processing techniques to the Automated Composition of Services field, especially in intelligent home environments (Cremene et al., 2009) These approaches offer mechanisms to map user words to basic functionalities of the system. In those works, the goal is to extract the workflow of the composite service from the User Request. The novelty of the application of Natural Language in this work is that in some scenarios, such as the early environmental warnings domain, the user can only express his desire or goal and not the workflow of the composite service. The workflow of the new composite service should be obtained from the automated composition process. Another element of particular relevance is the user preferences and the context of the situation. Automatically discerning the context of the request can enrich the process of automated composition processing by adding important information. In this regard, special words in user requests such as “urgently”, “danger” and “quickly” can represent a preference for service quality instead of cost. Secondly, data about the user location and the characteristics of the device may offer critical information about the preferred delivery format for the output of the composition of services.

In relation to the usefulness of automated services composition in this work, some considerations must be made. The Environmental management domain contains the following particularities: (i) composed services can be formed by Web Services and/or by basic Telecom features like Call or send SMS, (ii) the number of different services is rather limited due to the specialized field of action, (iii) in environmental management the procedures for emergencies handling and monitoring are standard. This means that these procedures and the associated services can be described using semantic annotations by experts, as the number of annotations needed is relatively few and standard. This is in fact a very important issue, since few service providers have taken up the opportunity to mark-up their web services, for the simple reason that they do not envisage the use of their services by an automated planning system (Carman, Serafini, & Traverso, 2003). Although the present architecture is focused on a particular domain, that is, the implementation is domain-dependent, most of the principles discussed here are applicable to similar scenarios.

The overall functioning of the architecture is as follows: the user request is received in natural language from a given device. The request is processed to determine the goals and preferences of the user. At the same time, information obtained from the sensors may be added to the request depending on the context. Next, the request is translated dynamically into a planning instance modeled using the Planning Domain Definition Language (PDDL) (Gerevini, Haslum, Long, Saetti, & Dimopoulos, 2009). Then, the PDDL formatted request is sent to the High Level Replanner module of the Planning, Learning and Execution Architecture (PELEA) (Guzmán et al., 2012). PELEA computes a plan using a domain-independent planner and manages its execution (note that the plan represents the composition of services). Finally, the composed plan is executed in a Jain SLEE 5 environment for convergent services.

In this context, the main contributions of our research work are: (1) A Framework for Advanced Telecom Services Composition based on Automated Planning; (2) a metamodel for user context including device characteristics, preferences and user profile information; (3) a mechanism that automatically translates user requests in Natural language into planning instances formulated in PDDL; and (4) the integration of a planning architecture in the execution of plans based on expert-made Mashups.

This paper is organized as follows: Section 2 presents the motivating scenario of early warning in environmental domain. As the AUTO framework is based in Automated Planning; In Section 3 the whole architecture of the framework is described. Section 4 exposes the background about planning and describes the planning domain construction. Section 5 describes the modeling of user request and his translation to PDDL. Section 6 Describes the Prototype and experimentation. Section 7 presents the related work and Section 8 draws the conclusions.