Conditional simple temporal networks with uncertainty and decisions

Abstract

A conditional simple temporal network with uncertainty (CSTNU) is a framework able to model temporal plans subject to both conditional constraints and uncertain durations. The combination of these two characteristics represents the uncontrollable part of the network. That is, before the network starts executing, we do not know completely which time points and constraints will be taken into consideration nor how long the uncertain durations will last. Dynamic controllability (DC) implies the existence of a strategy scheduling the time points of the network in real time depending on how the uncontrollable part behaves. Despite all this, CSTNUs fail to model temporal plans in which a few conditional constraints are under control and may therefore influence (or be influenced by) the uncontrollable part. To bridge this gap, this paper proposes conditional simple temporal networks with uncertainty and decisions (CSTNUDs) which introduce decision time points into the specification in order to operate on this conditional part under control. We model the dynamic controllability checking (DC-checking) of a CSTNUD as a two-player game in which each player makes his moves in his turn at a specific time instant. We give an encoding into timed game automata for a sound and complete DC-checking. We also synthesize memoryless execution strategies for CSTNUDs proved to be DC. The proposed approach is fully automated.

Resource Controllability of Business Processes Under Conditional Uncertainty

“…Simple Temporal Networks with Uncertainty (STNUs, [34,44]) add uncontrollable (but bounded) durations between pairs of temporal events (temporal uncertainty), whereas conditional simple temporal networks (CSTNs, [28,43]) extend STNs by labeling time points and constraints with conjunctions of literals whose truth value assignments are out of control (conditional uncertainty). Conditional simple temporal networks with uncertainty (CSTNUs, [27]) merge STNUs and CSTNs, whereas conditional simple temporal networks with uncertainty and decisions (CSTNUDs, [49,60]) encompass all previous formalisms by adding further Boolean propositions whose truth value assignments are under control. Weak controllability of CSTNUDs remains unexplored, whereas strong controllability is addressed in [56] and dynamic controllability in [49,60].…”

“…Conditional simple temporal networks with uncertainty (CSTNUs, [27]) merge STNUs and CSTNs, whereas conditional simple temporal networks with uncertainty and decisions (CSTNUDs, [49,60]) encompass all previous formalisms by adding further Boolean propositions whose truth value assignments are under control. Weak controllability of CSTNUDs remains unexplored, whereas strong controllability is addressed in [56] and dynamic controllability in [49,60].…”

“…Controllability of temporal workflows (TWFs) (i.e., WFs augmented with task duration constraints, delays and deadlines) addresses uncontrollable task durations and conditional uncertainty (i.e., the uncontrollable choice of the WF path to take at runtime). A possible way to check controllability of TWFs is by encoding the TWF into a corresponding temporal network to boil down the controllability of the TWF to that of the temporal network for which controllability checking algorithms exist (e.g., [7][8][9]19,23,37,49,52,53,60]). Like TWFs, in ACWFs conditional uncertainty models the uncontrollable choice of the WF path to take during execution.…”

Conditional Schedules for Processes with Temporal Constraints

“…Algorithms for other types of temporal constraint networks have been proposed in [2,37]. Algorithms for computing conditional schedules for these types of networks is subject of ongoing research.…”

“…dynamic controllability of process definitions with several This article is part of the topical collection "Future Data and Security Engineering 2019" guest edited by Tran Khanh Dang. sets of temporal constraints have been proposed for different flavors of process models, e.g., [2,3,6,8,15,26,27,30,37]. Nevertheless, an algorithm which is computationally feasible, both sound and complete and effectively computes a conditional schedule was still missing.…”