Christel Baier

TACAS Conference 2026 Conference Paper

Concurrent Permissive Strategy Templates

• Ashwani Anand
• Christel Baier
• Calvin Chau
• Sascha Klüppelholz
• Ali Mirzaei
• Satya Prakash Nayak
• Anne-Kathrin Schmuck

AAAI Conference 2026 Conference Paper

Temporal Properties of Conditional Independence in Dynamic Bayesian Networks

• Rajab Aghamov
• Christel Baier
• Joël Ouaknine
• Jakob Piribauer
• Mihir Vahanwala
• Isa Vialard

Dynamic Bayesian networks (DBNs) are compact graphical representations used to model probabilistic systems where interdependent random variables and their distributions evolve over time. In this paper, we study the verification of the evolution of conditional-independence (CI) propositions against temporal logic specifications. To this end, we consider two specification formalisms over CI propositions: linear temporal logic (LTL), and non-deterministic Büchi automata (NBAs). This problem has two variants. Stochastic CI properties take the given concrete probability distributions into account, while structural CI properties are viewed purely in terms of the graphical structure of the DBN. We show that deciding whether a stochastic CI proposition eventually holds is at least as hard as the Skolem problem for linear recurrence sequences, which is a long-standing open problem in number theory. On the other hand, we show that verifying the evolution of structural CI propositions against LTL and NBA specifications is in PSPACE, and is hard for both NP and coNP. We also identify natural restrictions on the graphical structure of the DBN that make the verification of structural CI properties tractable.

AAAI Conference 2025 Conference Paper

Formal Quality Measures for Predictors in Markov Decision Processes

• Christel Baier
• Sascha Klüppelholz
• Jakob Piribauer
• Robin Ziemek

In adaptive systems, predictors are used to anticipate changes in the system’s state or behavior that may require system adaption, e.g., changing its configuration or adjusting resource allocation. Therefore, the quality of predictors is crucial for the overall reliability and performance of the system under control. This paper studies predictors in systems exhibiting probabilistic and non-deterministic behavior modelled as Markov decision processes (MDPs). Main contributions are the introduction of quantitative notions that measure the effectiveness of predictors in terms of their average capability to predict the occurrence of failures or other undesired system behaviors. The average is taken over all memoryless policies. We study two classes of such notions. One class is inspired by concepts that have been introduced in statistical analysis to explain the impact of features on the decisions of binary classifiers (such as precision, recall, f-score). Second, we study a measure that borrows ideas from recent work on probability-raising causality in MDPs and determines the quality of a predictor by the fraction of memoryless policies under which (the set of states in) the predictor is a probability-raising cause for the considered failure scenario.

KR Conference 2025 Conference Paper

Model Checking Linear Temporal Logic with Standpoint Modalities

• Rajab Aghamov
• Christel Baier
• Toghrul Karimov
• Rupak Majumdar
• Joël Ouaknine
• Jakob Piribauer
• Timm Spork

Standpoint linear temporal logic (SLTL) is a recently introduced extension of classical linear temporal logic (LTL) with standpoint modalities. Intuitively, these modalities allow to express that, from agent a's standpoint, it is conceivable that a given formula holds. Besides the standard interpretation of the standpoint modalities we introduce four new semantics, which differ in the information an agent can extract from the history. We provide a general model checking algorithm applicable to SLTL under any of the five semantics. Furthermore we analyze the computational complexity of the corresponding model checking problems, obtaining PSPACE-completeness in three cases, which stands in contrast to the known EXPSPACE-completeness of the SLTL satisfiability problem.

AAAI Conference 2024 Conference Paper

Backward Responsibility in Transition Systems Using General Power Indices

• Christel Baier
• Roxane van den Bossche
• Sascha Klüppelholz
• Johannes Lehmann
• Jakob Piribauer

To improve reliability and the understanding of AI systems, there is increasing interest in the use of formal methods, e.g. model checking. Model checking tools produce a counterexample when a model does not satisfy a property. Understanding these counterexamples is critical for efficient debugging, as it allows the developer to focus on the parts of the program that caused the issue. To this end, we present a new technique that ascribes a responsibility value to each state in a transition system that does not satisfy a given safety property. The value is higher if the non-deterministic choices in a state have more power to change the outcome, given the behaviour observed in the counterexample. For this, we employ a concept from cooperative game theory – namely general power indices, such as the Shapley value – to compute the responsibility of the states. We present an optimistic and pessimistic version of responsibility that differ in how they treat the states that do not lie on the counterexample. We give a characterisation of optimistic responsibility that leads to an efficient algorithm for it and show computational hardness of the pessimistic version. We also present a tool to compute responsibility and show how a stochastic algorithm can be used to approximate responsibility in larger models. These methods can be deployed in the design phase, at runtime and at inspection time to gain insights on causal relations within the behavior of AI systems.

I&C Journal 2024 Journal Article

Entropic risk for turn-based stochastic games

• Christel Baier
• Krishnendu Chatterjee
• Tobias Meggendorfer
• Jakob Piribauer

Entropic risk (ERisk) is an established risk measure in finance, quantifying risk by an exponential re-weighting of rewards. We study ERisk for the first time in the context of turn-based stochastic games with the total reward objective. This gives rise to an objective function that demands the control of systems in a risk-averse manner. We show that the resulting games are determined and, in particular, admit optimal memoryless deterministic strategies. This contrasts risk measures that previously have been considered in the special case of Markov decision processes and that require randomization and/or memory. We provide several results on the decidability and the computational complexity of the threshold problem, i. e. whether the optimal value of ERisk exceeds a given threshold. Furthermore, an approximation algorithm for the optimal value of ERisk is provided.

IJCAI Conference 2023 Conference Paper

A Unifying Formal Approach to Importance Values in Boolean Functions

• Hans Harder
• Simon Jantsch
• Christel Baier
• Clemens Dubslaff

Boolean functions and their representation through logics, circuits, machine learning classifiers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debugging. In this paper, we introduce a uniform framework for reasoning about such values, relying on a generic notion of importance value functions (IVFs). The class of IVFs is defined by axioms motivated from several notions of importance values introduced in the literature, including Ben-Or and Linial’s influence and Chockler, Halpern, and Kupferman’s notion of responsibility and blame. We establish a connection between IVFs and game-theoretic concepts such as Shapley and Banzhaf values, both of which measure the impact of players on outcomes in cooperative games. Exploiting BDD-based symbolic methods and projected model counting, we devise and evaluate practical computation schemes for IVFs.

GandALF Workshop 2023 Workshop Paper

Counterfactual Causality for Reachability and Safety based on Distance Functions

• Julie Parreaux
• Jakob Piribauer
• Christel Baier

Investigations of causality in operational systems aim at providing human-understandable explanations of why a system behaves as it does. There is, in particular, a demand to explain what went wrong on a given counterexample execution that shows that a system does not satisfy a given specification. To this end, this paper investigates a notion of counterfactual causality in transition systems based on Stalnaker's and Lewis' semantics of counterfactuals in terms of most similar possible worlds and introduces a novel corresponding notion of counterfactual causality in two-player games. Using distance functions between paths in transition systems, this notion defines whether reaching a certain set of states is a cause for the violation of a reachability or safety property. Similarly, using distance functions between memoryless strategies in reachability and safety games, it is defined whether reaching a set of states is a cause for the fact that a given strategy for the player under investigation is losing. The contribution of the paper is two-fold: In transition systems, it is shown that counterfactual causality can be checked in polynomial time for three prominent distance functions between paths. In two-player games, the introduced notion of counterfactual causality is shown to be checkable in polynomial time for two natural distance functions between memoryless strategies. Further, a notion of explanation that can be extracted from a counterfactual cause and that pinpoints changes to be made to the given strategy in order to transform it into a winning strategy is defined. For the two distance functions under consideration, the problem to decide whether such an explanation imposes only minimal necessary changes to the given strategy with respect to the used distance function turns out to be coNP-complete and not to be solvable in polynomial time if P is not equal to NP, respectively.

MFCS Conference 2023 Conference Paper

Entropic Risk for Turn-Based Stochastic Games

• Christel Baier
• Krishnendu Chatterjee
• Tobias Meggendorfer
• Jakob Piribauer

Entropic risk (ERisk) is an established risk measure in finance, quantifying risk by an exponential re-weighting of rewards. We study ERisk for the first time in the context of turn-based stochastic games with the total reward objective. This gives rise to an objective function that demands the control of systems in a risk-averse manner. We show that the resulting games are determined and, in particular, admit optimal memoryless deterministic strategies. This contrasts risk measures that previously have been considered in the special case of Markov decision processes and that require randomization and/or memory. We provide several results on the decidability and the computational complexity of the threshold problem, i. e. whether the optimal value of ERisk exceeds a given threshold. In the most general case, the problem is decidable subject to Shanuel’s conjecture. If all inputs are rational, the resulting threshold problem can be solved using algebraic numbers, leading to decidability via a polynomial-time reduction to the existential theory of the reals. Further restrictions on the encoding of the input allow the solution of the threshold problem in NP∩coNP. Finally, an approximation algorithm for the optimal value of ERisk is provided.

IJCAI Conference 2023 Conference Paper

More for Less: Safe Policy Improvement with Stronger Performance Guarantees

• Patrick Wienhöft
• Marnix Suilen
• Thiago D. Simão
• Clemens Dubslaff
• Christel Baier
• Nils Jansen

In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated. State-of-the-art approaches to SPI require a high number of samples to provide practical probabilistic guarantees on the improved policy's performance. We present a novel approach to the SPI problem that provides the means to require less data for such guarantees. Specifically, to prove the correctness of these guarantees, we devise implicit transformations on the data set and the underlying environment model that serve as theoretical foundations to derive tighter improvement bounds for SPI. Our empirical evaluation, using the well-established SPI with baseline bootstrapping (SPIBB) algorithm, on standard benchmarks shows that our method indeed significantly reduces the sample complexity of the SPIBB algorithm.

JAIR Journal 2022 Journal Article

Admissibility in Probabilistic Argumentation

• Nikolai Käfer
• Christel Baier
• Martin Diller
• Clemens Dubslaff
• Sarah Alice Gaggl
• Holger Hermanns

Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion for classical reasoning in abstract argumentation frameworks, it has barely been reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of abstract argumentation based on probabilistic notions of admissibility. Our approach follows the natural idea of defining probabilistic semantics for abstract argumentation by systematically imposing constraints on the joint probability distribution on the sets of arguments, rather than on probabilities of single arguments. As a result, there might be either a uniquely defined distribution satisfying the constraints, but also none, many, or even an infinite number of satisfying distributions are possible. We provide probabilistic semantics corresponding to the classical complete and stable semantics and show how labeling schemes provide a bridge from distributions back to argument labelings. In relation to existing work on probabilistic argumentation, we present a taxonomy of semantic notions. Enabled by the constraint-based approach, standard reasoning problems for probabilistic semantics can be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation.

IJCAI Conference 2021 Conference Paper

A Game-Theoretic Account of Responsibility Allocation

• Christel Baier
• Florian Funke
• Rupak Majumdar

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.

KR Conference 2021 Conference Paper

Admissibility in Probabilistic Argumentation

• Christel Baier
• Martin Diller
• Clemens Dubslaff
• Sarah Alice Gaggl
• Holger Hermanns
• Nikolai Käfer

Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics, and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion in the classical setting, it has been merely reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of argumentation based on probabilistic notions of admissibility in a way that they form fully conservative extensions of classical notions. In particular, our building blocks are not the beliefs regarding single arguments. Instead we start from the fairly natural idea that whatever argumentation semantics is to be considered, semantics systematically induces constraints on the joint probability distribution on the sets of arguments. In some cases there might be many such distributions, even infinitely many ones, in other cases there may be one or none. Standard semantic notions are shown to induce such sets of constraints, and so do their probabilistic extensions. This allows them to be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation. We present a taxonomy of semantic notions, also in relation to published work, together with a running example illustrating our achievements.

AAAI Conference 2021 Conference Paper

Responsibility Attribution in Parameterized Markovian Models

• Christel Baier
• Florian Funke
• Rupak Majumdar

We consider the problem of responsibility attribution in the setting of parametric Markov chains. Given a family of Markov chains over a set of parameters, and a property, responsibility attribution asks how the difference in the value of the property should be attributed to the parameters when they change from one point in the parameter space to another. We formalize responsibility as path-based attribution schemes studied in cooperative game theory. An attribution scheme in a game determines how a value (a surplus or a cost) is distributed among a set of participants. Path-based attribution schemes include the well-studied Aumann-Shapley and the Shapley-Shubik schemes. In our context, an attribution scheme measures the responsibility of each parameter on the value function of the parametric Markov chain. We study the decision problem for path-based attribution schemes. Our main technical result is an algorithm for deciding if a path-based attribution scheme for a rational (ratios of polynomials) cost function is over a rational threshold. In particular, it is decidable if the Aumann-Shapley value for a player is at least a given rational number. As a consequence, we show that responsibility attribution is decidable for parametric Markov chains and for a general class of properties that include expectation and variance of discounted sum and long-run average rewards, as well as specifications in temporal logic.

GandALF Workshop 2021 Workshop Paper

Witnessing Subsystems for Probabilistic Systems with Low Tree Width

• Simon Jantsch
• Jakob Piribauer
• Christel Baier

A standard way of justifying that a certain probabilistic property holds in a system is to provide a witnessing subsystem (also called critical subsystem) for the property. Computing minimal witnessing subsystems is NP-hard already for acyclic Markov chains, but can be done in polynomial time for Markov chains whose underlying graph is a tree. This paper considers the problem for probabilistic systems that are similar to trees or paths. It introduces the parameters directed tree-partition width (dtpw) and directed path-partition width (dppw) and shows that computing minimal witnesses remains NP-hard for Markov chains with bounded dppw (and hence also for Markov chains with bounded dtpw). By observing that graphs of bounded dtpw have bounded width with respect to all known tree similarity measures for directed graphs, the hardness result carries over to these other tree similarity measures. Technically, the reduction proceeds via the conceptually simpler matrix-pair chain problem, which is introduced and shown to be NP-complete for nonnegative matrices of fixed dimension. Furthermore, an algorithm which aims to utilise a given directed tree partition of the system to compute a minimal witnessing subsystem is described. It enumerates partial subsystems for the blocks of the partition along the tree order, and keeps only necessary ones. A preliminary experimental analysis shows that it outperforms other approaches on certain benchmarks which have directed tree partitions of small width.

I&C Journal 2020 Journal Article

Parametric Markov chains: PCTL complexity and fraction-free Gaussian elimination

• Christel Baier
• Christian Hensel
• Lisa Hutschenreiter
• Sebastian Junges
• Joost-Pieter Katoen
• Joachim Klein

Parametric Markov chains have been introduced as a model for families of stochastic systems that rely on the same graph structure, but differ in the concrete transition probabilities. The latter are specified by polynomial constraints over a finite set of parameters. Important tasks in the analysis of parametric Markov chains are (1) computing closed-form solutions for reachability probabilities and other quantitative measures and (2) finding symbolic representations of the set of parameter valuations for which a given temporal logical formula holds as well as (3) the decision variant of (2) that asks whether there exists a parameter valuation where a temporal logical formula holds. Our contribution to (1) is to show that existing implementations for computing rational functions for reachability probabilities or expected costs in parametric Markov chains can be improved by using fraction-free Gaussian elimination, a long-known technique for linear equation systems with parametric coefficients. Our contribution to (2) and (3) is a complexity-theoretic discussion of the model-checking problem for parametric Markov chains and probabilistic computation tree logic (PCTL) formulas. We present an exponential-time algorithm for (2) and a PSPACE upper bound for (3). Moreover, we identify fragments of PCTL and subclasses of parametric Markov chains where (1) and (3) are solvable in polynomial time and establish NP-hardness for other PCTL fragments.

GandALF Workshop 2017 Workshop Paper

Parametric Markov Chains: PCTL Complexity and Fraction-free Gaussian Elimination

• Lisa Hutschenreiter
• Christel Baier
• Joachim Klein

Parametric Markov chains have been introduced as a model for families of stochastic systems that rely on the same graph structure, but differ in the concrete transition probabilities. The latter are specified by polynomial constraints for the parameters. Among the tasks typically addressed in the analysis of parametric Markov chains are (1) the computation of closed-form solutions for reachabilty probabilities and other quantitative measures and (2) finding symbolic representations of the set of parameter valuations for which a given temporal logical formula holds as well as (3) the decision variant of (2) that asks whether there exists a parameter valuation where a temporal logical formula holds. Our contribution to (1) is to show that existing implementations for computing rational functions for reachability probabilities or expected costs in parametric Markov chains can be improved by using fraction-free Gaussian elimination, a long-known technique for linear equation systems with parametric coefficients. Our contribution to (2) and (3) is a complexity-theoretic discussion of the model checking problem for parametric Markov chains and probabilistic computation tree logic (PCTL) formulas. We present an exponential-time algorithm for (2) and a PSPACE upper bound for (3). Moreover, we identify fragments of PCTL and subclasses of parametric Markov chains where (1) and (3) are solvable in polynomial time and establish NP-hardness for other PCTL fragments.

Highlights Conference 2015 Conference Abstract

Cost-Utility Analysis in Weighted Markovian Models

• Christel Baier

Various types of automata models with weights attached to the states and/or transitions have been introduced to model and analyze the resource-awareness and other quantitative phenomena of systems. In this context, weight accumulation appears as a natural concept to reason about cost and utility measures. The accumulation of non-negative weights can, for instance, serve to formalize the total energy consumption of a given task schedule or the total penalty to be paid for missed deadlines. Weight functions with negative and positive values can be used to model the energy level in battery-operated devices or the total win or loss of a share at the stock market over one day. The conceptual similarity between accumulated weights and counter machines causes the undecidability of many verification problems for multi-weighted models and temporal logics with weight accumulation over finite paths of unbounded length. However, decidability can be achieved for verification tasks in specialized structures, such as energy games or models with non-negative weight functions. Likewise, decidability results have been established for temporal logics with restricted forms of weight accumulation, such as modalities for weight accumulation along finite windows or limit-average properties. This extended abstract deals with discrete-time Markovian models and addresses algorithmic problems for a cost-utility analysis. First, it reports on results on linear temporal specifications with weight assertions. The second part addresses algorithms to compute optimal weight bounds for probabilistic reachability constraints and assertions on cost-utility ratios. 09: 30 09: 40 Break

TCS Journal 2006 Journal Article

Experiments with deterministic ω -automata for formulas of linear temporal logic

• Joachim Klein
• Christel Baier

This paper addresses the problem of generating deterministic ω -automata for formulas of linear temporal logic, which can be solved by applying well-known algorithms to construct a nondeterministic Büchi automaton for the given formula on which we then apply a determinization algorithm. We study here in detail Safra's determinization algorithm, present several heuristics that attempt to decrease the size of the resulting automata and report on experimental results.

LPAR Conference 2006 Conference Paper

On Computing Fixpoints in Well-Structured Regular Model Checking, with Applications to Lossy Channel Systems

• Christel Baier
• Nathalie Bertrand 0001
• Philippe Schnoebelen

Abstract We prove a general finite convergence theorem for “upward-guarded” fixpoint expressions over a well-quasi-ordered set. This has immediate applications in regular model checking of well-structured systems, where a main issue is the eventual convergence of fixpoint computations. In particular, we are able to directly obtain several new decidability results on lossy channel systems.

I&C Journal 2005 Journal Article

Comparative branching-time semantics for Markov chains

• Christel Baier
• Joost-Pieter Katoen
• Holger Hermanns
• Verena Wolf

This paper presents various semantics in the branching-time spectrum of discrete-time and continuous-time Markov chains (DTMCs and CTMCs). Strong and weak bisimulation equivalence and simulation pre-orders are covered and are logically characterized in terms of the temporal logics Probabilistic Computation Tree Logic (PCTL) and Continuous Stochastic Logic (CSL). Apart from presenting various existing branching-time relations in a uniform manner, this paper presents the following new results: (i) strong simulation for CTMCs, (ii) weak simulation for CTMCs and DTMCs, (iii) logical characterizations thereof (including weak bisimulation for DTMCs), (iv) a relation between weak bisimulation and weak simulation equivalence, and (v) various connections between equivalences and pre-orders in the continuous- and discrete-time setting. The results are summarized in a branching-time spectrum for DTMCs and CTMCs elucidating their semantics as well as their relationship.

TCS Journal 2005 Journal Article

Efficient computation of time-bounded reachability probabilities in uniform continuous-time Markov decision processes

• Christel Baier
• Holger Hermanns
• Joost-Pieter Katoen
• Boudewijn R. Haverkort

A continuous-time Markov decision process (CTMDP) is a generalization of a continuous-time Markov chain in which both probabilistic and nondeterministic choices co-exist. This paper presents an efficient algorithm to compute the maximum (or minimum) probability to reach a set of goal states within a given time bound in a uniform CTMDP, i. e. , a CTMDP in which the delay time distribution per state visit is the same for all states. It furthermore proves that these probabilities coincide for (time-abstract) history-dependent and Markovian schedulers that resolve nondeterminism either deterministically or in a randomized way.

I&C Journal 2005 Journal Article

Simulating perfect channels with probabilistic lossy channels

• Parosh Abdulla
• Christel Baier
• S. Purushothaman Iyer
• Bengt Jonsson

We consider the problem of deciding whether an infinite-state system (expressed as a Markov chain) satisfies a correctness property with probability 1. This problem is, of course, undecidable for general infinite-state systems. We focus our attention on the model of probabilistic lossy channel systems consisting of finite-state processes that communicate over unbounded lossy FIFO channels. Abdulla and Jonsson have shown that safety properties are decidable while progress properties are undecidable for non-probabilistic lossy channel systems. Under assumptions of “sufficiently high” probability of loss, Baier and Engelen have shown how to check whether a property holds of probabilistic lossy channel system with probability 1. In this paper, we consider a model of probabilistic lossy channel systems, where messages can be lost only during send transitions. In contrast to the model of Baier and Engelen, once a message is successfully sent to channel, it can only be removed through a transition which receives the message. We show that checking whether safety properties hold with probability 1 is undecidable for this model. Our proof depends upon simulating a perfect channel, with a high degree of confidence, using lossy channels.

TCS Journal 2001 Journal Article

Metric semantics for true concurrent real time

• Joost-Pieter Katoen
• Christel Baier
• Diego Latella

This paper investigates the use of a complete metric space framework for providing denotational semantics to a real-time process algebra. The study is carried out in a non-interleaving setting and is based on a timed extension of Langerak's bundle event structures, a variant of Winskel's event structures. The distance function of the metric is based on the amount of time to which event structures do ‘agree’. We show that this intuitive notion of distance is a pseudo-metric (but not a metric) on the set of timed event structures. A generalisation to equivalence classes of timed event structures in which we abstract from event identities and non-executable events (events that can never occur) is shown to be a complete ultra-metric space. We present an operational semantics for the considered language and show that the metric semantics is an abstraction of it. The operational semantics is characterised by the absence of synchronisation on the advance of time as opposed to the operational semantics of most real-time calculi. The consistency between our metric and an existing cpo-based denotational semantics is briefly investigated.

TCS Journal 1997 Journal Article

Trees and semantics

• Christel Baier

Several types of trees are proposed in the literature to model the behaviour of nondeterministic processes. The purpose of this paper is to clarify the distinction between the various types of trees and to discuss their use as semantic domains. Trees (in the sense of Milner's derivation trees) and bisimulation equivalence classes of trees are considered in three different settings: in the category of sets, in the metric setting and in the partial order setting. Each type of tree is characterized by a domain equation. Special attention is given to the use of trees as models for processes which allow for unbounded nondeterminism as for instance Milners CCS processes. In this case one has to deal with infinitely branching trees which cause problems to give denotational semantics that are fully abstract w. r. t. a transition system based operational semantics.

TCS Journal 1996 Journal Article

Metric completion versus ideal completion

• Mila E. Majster-Cederbaum
• Christel Baier

Complete partial orders have been used for a long time for defining semantics of programming languages. In the context of concurrency de Bakker and Zucker (1982) proposed a metric setting for handling concurrency, recursion and nontermination, which has proved to be very successful in many applications. Starting with a semantic domain D for ‘finite behaviour’ we investigate the relation between the ideal completion Idl(D) and the metric completion which are both suitable to model recursion and infinite behaviour. We also consider the properties of semantic operators.

TCS Journal 1994 Journal Article

Denotational semantics in the cpo and metric approach

• Christel Baier
• Mila E. Majster-Cederbaum

We investigate the implications of choosing a cpo-framework resp. a complete metric space framework for defining denotational semantics of languages that allow for recursion/iteration, communication and concurrency. We first establish a general framework for the cpo and the metric approach. The existence and uniqueness of meaning functions is studied. In the metric case the existence and uniqueness of a meaning function can be established under some reasonable assumptions. In the cpo-case we obtain the existence of a least meaning function. From these theorems consistency results can be concluded. In the second part we study the impact of the choice between cpo and metric for semantics based on event-structures and for semantics based on pomset classes.