Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Cognitive Business Process Management for Adaptive Cyber-Physical Processes

In the era of Big Data and Internet-of-Things (IoT), all real-world environments are gradually becoming cyber-physical (e.g., emergency management, healthcare, smart manufacturing, etc.), with the presence of connected devices and embedded ICT systems (e.g., smartphones, sensors, actuators) producing huge amounts of data and events that influence the enactment of the Cyber Physical Processes (CPPs) enacted in such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention at run-time. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS that combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on well-established action-based formalisms in Artificial Intelligence, which allow to interpret the ever-changing knowledge of cyber-physical environments and to adapt CPPs by preserving their base structure.Comment: Preprint from Proceedings of 1st International Workshop on Cognitive Business Process Management (CBPM 2017

SmartPM: Automated Adaptation of Dynamic Processes

In this demonstration paper, we present the first working version of SmartPM, a Process Management System that is able to automatically adapt dynamic processes at run-time when unanticipated exceptions occur, thus requiring no specification of recovery policies at design-time

SmartPM: An Adaptive Process Management System for Executing Processes in Cyber-Physical Domains

Nowadays, the automation of business processes not only spans classical business domains (e.g., banks and governmental agencies), but also new settings such as healthcare, smart manufacturing, domotics and emergency management [2]. Such domains are characterized by the presence of a Cyber-Physical System (CPS) coordinating heterogeneous ICT components with a large variety of architectures, sensors, actuators, computing and communication capabilities, and involving real world entities that perform complex tasks in the "physical" real world to achieve a common goal. In this context, Process Management Systems (PMSs) are used to manage the life cycle of the processes that coordinate the services offered by the CPS to the real world entities, on the basis of the contextual information collected from the specific cyber-physical domain of interest. The physical world, however, is not entirely predictable. CPSs do not necessarily and always operate in a controlled environment, and their processes must be robust to unexpected conditions and adaptable to exceptions and external exogenous events. In this paper, we tackle the above issue by introducing the SmartPM System (http://www.dis.uniroma1.it/smartpm) an adaptive PMS which combines process execution monitoring, unanticipated exception detection (without requiring an explicit definition of exception handlers), and automated resolution strategies on the basis of well-established Artificial Intelligence techniques, including the Situation Calculus and IndiGolog [1], and classical planning [3]

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

SmartPM: automatic adaptation of dynamic processes at run-time

The research activity outlined in this thesis is devoted to define a general approach, a concrete architecture and a prototype Process Management System (PMS) for the automated adaptation of dynamic processes at run-time, on the basis of a declarative specification of process tasks and relying on well-established reasoning about actions and planning techniques. The purpose is to demonstrate that the combination of procedural and imperative models with declarative elements, along with the exploitation of techniques from the field of artificial intelligence (AI), such as Situation Calculus, IndiGolog and automated planning, can increase the ability of existing PMSs of supporting dynamic processes. To this end, a prototype PMS named SmartPM, which is specifically tailored for supporting collaborative work of process participants during pervasive scenarios, has been developed. The adaptation mechanism deployed on SmartPM is based on execution monitoring for detecting failures at run-time, which does not require the definition of the adaptation strategy in the process itself (as most of the current approaches do), and on automatic planning techniques for the synthesis of the recovery procedure

Adaptive Process Management in Highly Dynamic and Pervasive Scenarios

Process Management Systems (PMSs) are currently more and more used as a supporting tool for cooperative processes in pervasive and highly dynamic situations, such as emergency situations, pervasive healthcare or domotics/home automation. But in all such situations, designed processes can be easily invalidated since the execution environment may change continuously due to frequent unforeseeable events. This paper aims at illustrating the theoretical framework and the concrete implementation of SmartPM, a PMS that features a set of sound and complete techniques to automatically cope with unplanned exceptions. PMS SmartPM is based on a general framework which adopts the Situation Calculus and Indigolog

Design and Realization of a Sensor-aware Task List Handler for Adaptive Processes in Cyber-Physical Environments

Protsesside juhtimise süsteemid leiavad aina enam kasutust toetamaks muutlike situatsioone ja koostööd nõudvaid protsesse. Mõned valdkonnad on väga muutlikud oma keskkonna poolest, võides muutuda protsessi jooksul ja seega mõjutada töövoogu moel, mil protsessiga pole enam võimalik jätkata. Sellistes valdkondades tegelevad näiteks hädaabi, päästekomandod, kiirabi ja teised. Taolised meeskonnad koosnevad üldjuhul vastavalt tegevuskohale opereerivatest osalejatest. Nendes valdkondades on oodamatute sündmuste sagedus ja erinevus väga suur võrreldes tavapäraste äriprotsessidega mida praegused äriprotsesside juhtimise lahendused hallata suudavad. 2011. aastal tutvustati Rooma Sapienza Ülikoolis esialgset SmartPM (Tark Protsesside Juhtija) konseptsiooni tõestavat prototüüpi ja mudelit mis suudab automaatselt kohanduda planeerimata muutustega. Pidev reaalmaailma muutujate jälgimine on vajalik taolistes valdkondades. Küber-füüsilise süsteemi loomine aitab seda automatiseerida, luues füüsilisest-digitaalseks silla. See sild võib olla tööriistade kogum mis koosneb sensoritest, mobiilsetest seadmetest ja tõlkivast kihist et võtta reaalmaailmast informatsioon ja muuta see digitaalsele süsteemile mõistetavaks. Probleem tekib sensoritelt tuleva informatsiooni tõlkimisel kuna digitaalne süsteem töötleb ainult diskreetseid väärtuseid, aga sensoritelt tulev informatsioon on üldjuhul pidev. Selle probleemi lahendamiseks pakkus autor välja ja implementeeris konkreetse lahenduse. Käesolev töö tutvustab lähemalt sensori-teadliku ülesannete juhtijat ja veebitööriista (mis loodi lahendamaks reaalmaailma väärtuste diskretiseermise probleemi) arhitektuuri ja implementatsiooni. Samuti seletatakse kuidas käesoleva töö tulemusena täiendati ja uuendati kohanevat protsesside juhtimise süsteemi, SmartPMi.Process Management Systems (PMSs) are more and more used to support highly dynamic situations and cooperative processes. Some domains have great diversity of environment variables that can change during the process and therefore affect the workflow in a way that process can not be successfully carried out. Such can be emergency management, health care and other domains involving in most cases in-field actors. In those domains, the frequency and variety of unexpected changes is really high compared to classical business domains that current Business Process Management (BPM) solutions can handle. In 2011, a model and an initial proofof-concept prototype of SmartPM (Smart Process Management) was introduced in Sapienza - Universit´a di Roma that is able to automatically cope with unplanned changes. The continuous screening of the real-world factors is suggested for such domains. A cyber-physical system can be created to automate the screening via physical-to-digital bridge. This bridge can be a set of tools consisting of sensors, mobile devices and translation layer to extract and feed the real-world information to the digital system. Challenge arises when transferring the information from sensors to the system as the system works with discrete values, but the information gathered by the sensors is continuous in most cases. To target this problem, a concrete solution is proposed and implemented by the author. This thesis explains the architecture and implementation of the sensor-aware task list handler and the web tool approach that was created to solve the discretization challenge of the real-world values. It is also explained how the adaptive PMS, SmartPM, was further developed and updated as the contribution of this thesis

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle