Incorporating Decision Nodes into Conditional Simple Temporal Networks

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include special time-points, called observation time-points. In a CSTN, the agent executing the network controls the execution of every time-point. However, each observation time-point has a unique propositional letter associated with it and, when the agent executes that time-point, the environment assigns a truth value to the corresponding letter. Thus, the agent observes but, does not control the assignment of truth values. A CSTN is dynamically consistent (DC) if there exists a strategy for executing its time-points such that all relevant constraints will be satisfied no matter which truth values the environment assigns to the propositional letters. Alternatively, in a Labeled Simple Temporal Network (Labeled STN) - also called a Temporal Plan with Choice - the agent executing the network controls the assignment of values to the so-called choice variables. Furthermore, the agent can make those assignments at any time. For this reason, a Labeled STN is equivalent to a Disjunctive Temporal Network. This paper incorporates both of the above extensions by augmenting a CSTN to include not only observation time-points but also decision time-points. A decision time-point is like an observation time-point in that it has an associated propositional letter whose value is determined when the decision time-point is executed. It differs in that the agent - not the environment - selects that value. The resulting network is called a CSTN with Decisions (CSTND). This paper shows that a CSTND generalizes both CSTNs and Labeled STNs, and proves that the problem of determining whether any given CSTND is dynamically consistent is PSPACE-complete. It also presents algorithms that address two sub-classes of CSTNDs: (1) those that contain only decision time-points; and (2) those in which all decisions are made before execution begins

Checking Dynamic Consistency of Conditional Hyper Temporal Networks via Mean Payoff Games (Hardness and (pseudo) Singly-Exponential Time Algorithm)

In this work we introduce the \emph{Conditional Hyper Temporal Network (CHyTN)} model, which is a natural extension and generalization of both the \CSTN and the \HTN model. Our contribution goes as follows. We show that deciding whether a given \CSTN or CHyTN is dynamically consistent is \coNP-hard. Then, we offer a proof that deciding whether a given CHyTN is dynamically consistent is \PSPACE-hard, provided that the input instances are allowed to include both multi-head and multi-tail hyperarcs. In light of this, we continue our study by focusing on CHyTNs that allow only multi-head or only multi-tail hyperarcs, and we offer the first deterministic (pseudo) singly-exponential time algorithm for the problem of checking the dynamic-consistency of such CHyTNs, also producing a dynamic execution strategy whenever the input CHyTN is dynamically consistent. Since \CSTN{s} are a special case of CHyTNs, this provides as a byproduct the first sound-and-complete (pseudo) singly-exponential time algorithm for checking dynamic-consistency in CSTNs. The proposed algorithm is based on a novel connection between CSTN{s}/CHyTN{s} and Mean Payoff Games. The presentation of the connection between \CSTN{s}/CHyTNs and \MPG{s} is mediated by the \HTN model. In order to analyze the algorithm, we introduce a refined notion of dynamic-consistency, named $\epsilon$-dynamic-consistency, and present a sharp lower bounding analysis on the critical value of the reaction time $\hat{\varepsilon}$ where a \CSTN/CHyTN transits from being, to not being, dynamically consistent. The proof technique introduced in this analysis of $\hat{\varepsilon}$ is applicable more generally when dealing with linear difference constraints which include strict inequalities.Comment: arXiv admin note: text overlap with arXiv:1505.0082

Temporal and Resource Controllability of Workflows Under Uncertainty

Workflow technology has long been employed for the modeling, validation and execution of business processes. A workflow is a formal description of a business process in which single atomic work units (tasks), organized in a partial order, are assigned to processing entities (agents) in order to achieve some business goal(s). Workflows can also employ workflow paths (projections with respect to a total truth value assignment to the Boolean variables associated to the conditional split connectors) in order (not) to execute a subset of tasks. A workflow management system coordinates the execution of tasks that are part of workflow instances such that all relevant constraints are eventually satisfied. Temporal workflows specify business processes subject to temporal constraints such as controllable or uncontrollable durations, delays and deadlines. The choice of a workflow path may be controllable or not, considered either in isolation or in combination with uncontrollable durations. Access controlled workflows specify workflows in which users are authorized for task executions and authorization constraints say which users remain authorized to execute which tasks depending on who did what. Access controlled workflows may consider workflow paths too other than the uncertain availability of resources (users, throughout this thesis). When either a task duration or the choice of the workflow path to take or the availability of a user is out of control, we need to verify that the workflow can be executed by verifying all constraints for any possible combination of behaviors arising from the uncontrollable parts. Indeed, users might be absent before starting the execution (static resiliency), they can also become so during execution (decremental resiliency) or they can come and go throughout the execution (dynamic resiliency). Temporal access controlled workflows merge the two previous formalisms by considering several kinds of uncontrollable parts simultaneously. Authorization constraints may be extended to support conditional and temporal features. A few years ago some proposals addressed the temporal controllability of workflows by encoding them into temporal networks to exploit "off-the-shelf" controllability checking algorithms available for them. However, those proposals fail to address temporal controllability where the controllable and uncontrollable choices of workflow paths may mutually influence one another. Furthermore, to the best of my knowledge, controllability of access controlled workflows subject to uncontrollable workflow paths and algorithms to validate and execute dynamically resilient workflows remain unexplored. To overcome these limitations, this thesis goes for exact algorithms by addressing temporal and resource controllability of workflows under uncertainty. I provide several new classes of (temporal) constraint networks and corresponding algorithms to check their controllability. After that, I encode workflows into these new formalisms. I also provide an encoding into instantaneous timed games to model static, decremental and dynamic resiliency and synthesize memoryless execution strategies. I developed a few tools with which I carried out some initial experimental evaluations

28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

The 28th International Symposium on Temporal Representation and Reasoning (TIME 2021) was planned to take place in Klagenfurt, Austria, but had to move to an online conference due to the insecurities and restrictions caused by the pandemic. Since its frst edition in 1994, TIME Symposium is quite unique in the panorama of the scientifc conferences as its main goal is to bring together researchers from distinct research areas involving the management and representation of temporal data as well as the reasoning about temporal aspects of information. Moreover, TIME Symposium aims to bridge theoretical and applied research, as well as to serve as an interdisciplinary forum for exchange among researchers from the areas of artifcial intelligence, database management, logic and verifcation, and beyond

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

On the implicit learnability of knowledge

The deployment of knowledge-based systems in the real world requires addressing the challenge of knowledge acquisition. While knowledge engineering by hand is a daunting task, machine learning has been proposed as an alternative. However, learning explicit representations for real-world knowledge that feature a desirable level of expressiveness remains difficult and often leads to heuristics without robustness guarantees. Probably Approximately Correct (PAC) Semantics offers strong guarantees, however learning explicit representations is not tractable, even in propositional logic. Previous works have proposed solutions to these challenges by learning to reason directly, without producing an explicit representation of the learned knowledge. Recent work on so-called implicit learning has shown tremendous promise in obtaining polynomial-time results for fragments of first-order logic, bypassing the intractable step of producing an explicit representation of learned knowledge. This thesis extends these ideas to richer logical languages such as arithmetic theories and multi-agent logics. We demonstrate that it is possible to learn to reason efficiently for standard fragments of linear arithmetic, and we establish a general finding that provides an efficient reduction from the learning-to-reason problem for any logic to any sound and complete solver for that logic. We then extend implicit learning in PAC Semantics to handle noisy data in the form of intervals and threshold uncertainty in the language of linear arithmetic. We prove that our extended framework maintains existing polynomial-time complexity guarantees. Furthermore, we provide the first empirical investigation of this purely theoretical framework. Using benchmark problems, we show that our implicit approach to learning optimal linear programming objective constraints significantly outperforms an explicit approach in practice. Our results demonstrate the effectiveness of PAC Semantics and implicit learning for real-world problems with noisy data and provide a path towards robust learning in expressive languages. Development in reasoning about knowledge and interactions in complex multi-agent systems spans domains such as artificial intelligence, smart traffic, and robotics. In these systems, epistemic logic serves as a formal language for expressing and reasoning about knowledge, beliefs, and communication among agents, yet integrating learning algorithms within multi-agent epistemic logic is challenging due to the inherent complexity of distributed knowledge reasoning. We provide proof of correctness for our learning procedure and analyse the sample complexity required to assert the entailment of an epistemic query. Overall, our work offers a promising approach to integrating learning and deduction in a range of logical languages from linear arithmetic to multi-agent epistemic logics

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Effective SAT solving

A growing number of problem domains are successfully being tackled by SAT solvers. This thesis contributes to that trend by pushing the state-of-the-art of core SAT algorithms and their implementation, but also in several important application areas. It consists of five papers: the first details the implementation of the SAT solver MiniSat and the other four papers discuss specific issues related to different application domains. In the first paper, catering to the trend of extending and adapting SAT solvers, we present a detailed description of MiniSat, a SAT solver designed for that particular purpose. The description additionally bridges a gap between theory and practice, serving as a tutorial on modern SAT solving algorithms. Among other things, we describe how to solve a series of related SAT problems efficiently, called incremental SAT solving. For finding finite first order models the MACE-style method that is based on SAT solving is well-known. In the second paper we improve the basic method with several techniques that can be loosely classified as either transformations that make the reduction to SAT result in fewer clauses or techniques that are designed to speed up the search of the SAT solver. The resulting tool, called Paradox, won the SAT/Models division of the CASC competition in 2003 and has not been beaten since by a single general purpose model finding tool. In the last decade the interest in methods for safety property verification that are based on SAT solving has been steadily growing. One example of such a method is temporal induction. The method requires a sequence of increasingly stronger induction proofs to be performed. In the third paper we show how this sequence of proofs can be solved efficiently using incremental SAT solving. The last two papers consider two frequently occurring types of encodings: (1) the problem of encoding circuits into CNF, and (2) encoding 0-1 integer linear programming into CNF and how to use incremental SAT to solve the intended ptimization problem. There are several encoding patterns that occur over and over again in this thesis but also elsewhere. The most noteworthy are: incremental SAT, lazy encoding of constraints, and bit-wise encoding of arithmetic influenced by hardware designs for adders and multipliers. The general conclusion is: deploying SAT solvers effectively requires implementations that are efficient, yet easily adaptable to specific application needs. Moreover, to get the best results, it is worth spending effort to make sure that one uses the best codings possible for an application. However, it is important to note that this is not absolutely necessary. For some applications naive problem codings work just fine which is indeed part of the appeal of using SAT solving

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency