Traditional model-checking techniques typically verify distributed algorithms only for a fixed number of finite-state processes. Parameterized model checking generalizes this to any number of processes, while still typically assuming that each process is finite-state. In this work, we consider asynchronous round-based distributed algorithms in which each process is infinite-state since it can execute for an infinite number of rounds. We show that the parameterized verification problem for asynchronous round-based distributed algorithms is undecidable, already for simple specifications. Nevertheless, as our main contribution, we provide a reduction to LTL model checking over finite-counter systems and prove that it is sound and complete. This enables the use of off-the-shelf, mature symbolic model checkers for finite-counter systems. We demonstrate the practical applicability of this reduction by verifying safety and liveness properties of several asynchronous round-based consensus and leader-election algorithms using the nuXmv model checker.

We study a model of Agentic AI, building on LTL synthesis originally studied in formal methods, that consists of autonomous agents with independent sequential decision-making capabilities. Specifically, we associate with each agent a goal expressed in LTL, and assumptions on the strategies employed by its peers and that the agent can exploit while synthesizing a strategy to realize its goal. While we can solve the synthesis problem under assumptions for each such agent we are not only interested in (1) synthesizing strategies for individual agents. Indeed, assumptions in turn are recursively defined through these strategy spaces. Importantly, we do not assume the ability to access or analyze an agent's internal strategy, as we make no assumptions about the nature of the decision makers, which may be, for example, ML-based. Instead, we focus on (2) characterizing the set of traces that are generated by strategies that realize the specification assigned to each agent. Using this characterization, we are able to (3) verify that the whole system, when in execution, satisfies a global objective, regardless of the strategies chosen by the agents from their allowed spaces. Moreover, by observing the evolution of the execution trace, we can (4) identify whether an agent makes a move that violates its specification and assign precise responsibility for the violation. Technically, we present automata-theoretic techniques to solve these problems, and show that each of them is 2EXPTIME-complete, matching the complexity of classical LTL synthesis.

We study two logics, LTLf+ and PPLTL+, to express properties of infinite traces, that are based on the linear-time temporal logics LTLf and PPLTL on finite traces. LTLf+/PPLTL+ use levels of Manna and Pnueli’s LTL safety-progress hierarchy, and thus have the same expressive power as LTL. However, they also retain a crucial characteristic of reactive synthesis for the base logics: the game arena for strategy extraction can be derived from deterministic finite automata (DFA). Consequently, these logics circumvent the notorious difficulties associated with determinizing infinite trace automata, typical of LTL synthesis. We present optimal DFA-based technique for solving reactive synthesis for LTLf+ and PPLTL+. Additionally, we adapt these algorithms to optimally solve satisfiability and model-checking for these two logics.

Explainable Artificial Intelligence (XAI) is critical for attaining trust in the operation of AI systems. A key question of an AI system is ``why was this decision made this way''. Formal approaches to XAI use a formal model of the AI system to identify abductive explanations. While abductive explanations may be applicable to a large number of inputs sharing the same concrete values, more general explanations may be preferred for numeric inputs. So-called inflated abductive explanations give intervals for each feature ensuring that any input whose values fall withing these intervals is still guaranteed to make the same prediction. Inflated explanations cover a larger portion of the input space, and hence are deemed more general explanations. But there can be many (inflated) abductive explanations for an instance. Which is the best? In this paper, we show how to find a most general abductive explanation for an AI decision. This explanation covers as much of the input space as possible, while still being a correct formal explanation of the model's behaviour. Given that we only want to give a human one explanation for a decision, the most general explanation gives us the explanation with the broadest applicability, and hence the one most likely to seem sensible.

We investigate LTL synthesis under structured assumptions about the environment. In our setting, the environment is viewed by the protagonist as a collection of peer agents acting together in a shared world. In contrast to the symmetrical frameworks typically studied in multi-agent systems, we take a strikingly asymmetric first-person perspective in which the protagonist ascribes a specification to each of its peer agents and the world, capturing its understanding of their possible strategies. We show that in this setting, LTL synthesis has the same computational complexity as standard LTL synthesis, i.e., 2EXPTIME-complete. We establish this via a sophisticated, yet fully implementable, argument that builds on the notion of traces compatible with strategies: we use the fact that if the basic specification of the world and of each agent is given in LTL then the sets of traces compatible with the strategies describing the behaviors of the agents are omega-regular. This enables the use of word-automata rather than the more complicated tree-automata.

In this paper, we study ltl f synthesis under environment specifications for arbitrary reachability and safety properties. We consider both kinds of properties for both agent tasks and environment specifications, providing a complete landscape of synthesis algorithms. For each case, we devise a specific algorithm (optimal wrt complexity of the problem) and prove its correctness. The algorithms combine common building blocks in different ways. While some cases are already studied in literature others are studied here for the first time.

We study the complexity of the model-checking problem for parameterized discrete-timed systems with arbitrarily many anonymous and identical processes, with and without a distinguished "controller", and communicating via synchronous rendezvous. Our framework extends the seminal work from German and Sistla on untimed systems by adding discrete-time clocks to processes. For the case without a controller, we show that the systems can be efficiently simulated -- and vice versa -- by systems of untimed processes that communicate via rendezvous and symmetric broadcast, which we call "RB-systems". Symmetric broadcast is a novel communication primitive that allows all processes to synchronize at once; however, it does not distinguish between sending and receiving processes. We show that the parameterized model-checking problem for safety specifications is pspace-complete, and for liveness specifications it is decidable in exptime. The latter result is proved using automata theory, rational linear programming, and geometric reasoning for solving certain reachability questions in a new variant of vector addition systems called "vector rendezvous systems". We believe these proof techniques are of independent interest and will be useful in solving related problems. For the case with a controller, we show that the parameterized model-checking problems for RB-systems and systems with asymmetric broadcast as a primitive are inter-reducible. This allows us to prove that for discrete timed-networks with a controller the parameterized model-checking problem is undecidable for liveness specifications. Our work exploits the intimate connection between parameterized discrete-timed systems and systems of processes communicating via broadcast, providing a rare and surprising decidability result for liveness properties of parameterized timed-systems, as well as extend work from untimed systems to timed systems.

Classical planning problems are defined using some specification language, such as PDDL. The domain expert defines action schemas, objects, the initial state, and the goal. One key aspect of PDDL is that the set of objects cannot be modified during plan execution. While this is fine in many domains, sometimes it makes modeling more complicated. This may impact the performance of planners, and it requires the domain expert to bound the number of required objects beforehand, which can be a challenge. We introduce an extension to the classical planning formalism, where action effects can create and remove objects. This problem is semi-decidable, but it becomes decidable if we can bound the number of objects in any given state, even though the state space is still infinite. On the practical side, we extend the Powerlifted planning system to support this PDDL extension. Our results show that this extension improves the performance of Powerlifted while supporting more natural PDDL models.

We study synthesis and verification of probabilistic models and specifications over finite traces. Probabilistic models are formalized in this work as Markov Chains and Markov Decisions Processes. Motivated by the recent attention given to, and importance of, finite-trace specifications in AI, we use linear-temporal logic on finite traces as a specification formalism for properties of traces with finite but unbounded time horizons. Since there is no bound on the time horizon, our Markov chains generate infinite traces, and we consider two possible semantics: “existential (resp. universal) prefix- semantics” which says that the finite-trace property holds on some (resp. every) finite prefix of the trace. For both types of semantics, we study two computational problems: the verification problem — “does a given Markov chain satisfy the specification with probability one?”; and the synthesis problem — “find a strategy (if there is one) that ensures the Markov decision process satisfies the specification with probability one”. We provide optimal algorithms that follow an automata-theoretic approach, and prove that the complexity of the synthesis problem is 2EXPTIME-complete for both semantics, and that for the verification problem it is PSPACE-complete for the universal-prefix semantics, but EXPSPACE-complete for the existential-prefix semantics.

To help it achieve its goal, an agent exploits assumptions it has about the behavior of its environment. The common view in planning and reactive synthesis is that such assumptions are sets of traces. This trace-centric view has the advantage of having well-understood specification formalisms, such as linear-time temporal logic. An alternative view, that we have promoted as being conceptually superior, is strategy-centric: assumptions are non-empty sets of environment strategies. In this work we relate these views and show that the strategy-centric view is a refinement of the trace-centric view. We thus address the following fundamental question: when should a set of traces be considered an assumption that the agent has about the environment's behavior? Our answer is in terms of coverability: every trace in the set should be consistent with some environment strategy that enforces it. We call such sets ``proper environment specifications''. Typical examples are given by (the traces consistent with a given) planning domain, and fairness constraints, but not arbitrary trace constraints. We provide an algorithm that, given a specification in linear-time temporal logic (LTL) decides whether or not it is a proper environment specification. Furthermore, we show that every set of traces has a ``proper environment core'', which excludes traces that the agent can ignore when devising its plan. We provide an algorithm for computing a representation of the core of an LTL formula, and prove that the core of an LTL-definable property is itself LTL-definable.

We study the synthesis under environment specifications problem for LTL/LTLf which, in particular, generalizes FOND (strong) planning with these temporal goals. We consider the case where the agent cannot enforce its goal --- for which the argument for using best-effort strategies has been made --- and study the intermediate ground, between enforcing and best-effort strategies, of dominant strategies. Intuitively, such strategies achieve the goal against any environment for which it is achievable. We show that dominant strategies may exist when enforcing ones do not, while still sharing with the latter many desirable properties such as being interchangeable with each other, and being monotone with respect to tightening of environment specifications. We give necessary and sufficient conditions for the existence of dominant strategies, and show that deciding if they exist is 2EXPTIME-complete --- the same as for enforcing strategies. Finally, we give a uniform, optimal, game-theoretic algorithm for simultaneously solving the three synthesis problems of enforcing, dominant, and best-effort strategies.

We study reactive systems with Borel goals operating in a possibly non-Markovian stochastic environment. Moreover, the specific environment is not known, only its support is, i.e., at each step one knows which transitions are possible and which are impossible, but the probability distribution amongst the possible transitions is unknown. We consider system strategies that are maximal in the dominance order, i.e., no other strategy achieves the goal with at least the same probability in all environments, and with a higher probability in some environment. We call such strategies "stochastic best-effort". We prove the very general result that stochastic best-effort strategies exist for any Borel goal. We do this by providing local characterizations in terms of a three-valued abstraction of the probability of achieving the goal at a history. The correctness of the characterization is shown using a version of the Lebesgue Density Theorem from geometric measure theory. On the more practical side, we consider goals given in linear temporal logic. We establish the computational complexity of synthesizing a stochastic best-effort strategy, and show that it is not harder than synthesizing an optimal strategy in a domain with fixed known probabilities.

Recent work has unveiled a theory for reasoning about the decisions made by binary classifiers: a classifier describes a Boolean function, and the reasons behind an instance being classified as positive are the prime-implicants of the function that are satisfied by the instance. One drawback of these works is that they do not explicitly treat scenarios where the underlying data is known to be constrained, e.g., certain combinations of features may not exist, may not be observable, or may be required to be disregarded. We propose a more general theory, also based on prime-implicants, tailored to taking constraints into account. The main idea is to view classifiers as describing partial Boolean functions that are undefined on instances that do not satisfy the constraints. We prove that this simple idea results in more parsimonious reasons. That is, not taking constraints into account (e.g., ignoring, or taking them as negative instances) results in reasons that are subsumed by reasons that do take constraints into account. We illustrate this improved succinctness on synthetic classifiers and classifiers learnt from real data.

We introduce the formula-synthesis problem for Propositional Dynamic Logic with Shuffle (PDL || ). This problem, which generalises the model-checking problem againsts PDL || is the following: given a finite transition system and a regular term-grammar that generates (possibly infinitely many) PDL || formulas, find a formula generated by the grammar that is true in the structure (or return that there is none). We prove that the problem is undecidable in general, but add certain restrictions on the input structure or on the input grammar to yield decidability. In particular, we prove that (1) if the grammar only generates formulas in PDL (without shuffle), then the problem is EXPTIME-complete, and a further restriction to linear grammars is PSPACE-complete, and a further restriction to non-recursive grammars is NP-complete, and (2) if one restricts the input structure to have only simple paths then the problem is in 2-EXPTIME. This work is motivated by and opens up connections to other forms of synthesis from hierarchical descriptions, including HTN problems in Planning and Attack-tree Synthesis problems in Security.

``Strong-cyclic policies" were introduced to formalize trial-and-error strategies and are known to work in Markovian stochastic domains, i.e., they guarantee that the goal is reached with probability 1. We introduce ``best-effort" policies for (not necessarily Markovian) stochastic domains. These generalize strong-cyclic policies by taking advantage of stochasticity even if the goal cannot be reached with probability 1. We compare such policies with optimal policies, i.e., policies that maximize the probability that the goal is achieved, and show that optimal policies are best-effort, but that the converse is false in general. With this framework at hand, we revisit the foundational problem of what it means to plan in nondeterministic domains when the nondeterminism has a stochastic nature. We show that one can view a nondeterministic planning domain as a representation of infinitely many stochastic domains with the same support but different probabilities, and that for temporally extended goals expressed in LTL/LTLf a finite-state best-effort policy in one of these domains is best-effort in each of the domains. In particular, this gives an approach for finding such policies that reduces to solving finite-state MDPs with LTL/LTLf goals. All this shows that ``best-effort" policies are robust to changes in the probabilities, as long as the support is unchanged.

We study best-effort synthesis under environment assumptions specified in LTL, and show that this problem has exactly the same computational complexity of standard LTL synthesis: 2EXPTIME-complete. We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). The latter are particularly well suited for implementation.

We study the semantics of knowledge in strategic reasoning. Most existing works either implicitly assume that agents do not know one another’s strategies, or that all strategies are known to all; and some works present inconsistent mixes of both features. We put forward a novel semantics for Strategy Logic with Knowledge that cleanly models whose strategies each agent knows. We study how adopting this semantics impacts agents’ knowledge and strategic ability, as well as the complexity of the model-checking problem.

We formally introduce and solve the synthesis problem for LTL goals in the case of multiple, even contradicting, assumptions about the environment. Our solution concept is based on ``best-effort strategies'' which are agent plans that, for each of the environment specifications individually, achieve the agent goal against a maximal set of environments satisfying that specification. By means of a novel automata theoretic characterization we demonstrate that this best-effort synthesis for multiple environments is 2ExpTime-complete, i.e., no harder than plain LTL synthesis. We study an important case in which the environment specifications are increasingly indeterminate, and show that as in the case of a single environment, best-effort strategies always exist for this setting. Moreover, we show that in this setting the set of solutions are exactly the strategies formed as follows: amongst the best-effort agent strategies for ɸ under the environment specification E1, find those that do a best-effort for ɸ under (the more indeterminate) environment specification E2, and amongst those find those that do a best-effort for ɸ under the environment specification E3, etc.

We study alternating automata with qualitative semantics over infinite binary trees: Alternation means that two opposing players construct a decoration of the input tree called a run, and the qualitative semantics says that a run of the automaton is accepting if almost all branches of the run are accepting. In this article, we prove a positive and a negative result for the emptiness problem of alternating automata with qualitative semantics. The positive result is the decidability of the emptiness problem for the case of Büchi acceptance condition. An interesting aspect of our approach is that we do not extend the classical solution for solving the emptiness problem of alternating automata, which first constructs an equivalent non-deterministic automaton. Instead, we directly construct an emptiness game making use of imperfect information. The negative result is the undecidability of the emptiness problem for the case of co-Büchi acceptance condition. This result has two direct consequences: the undecidability of monadic second-order logic extended with the qualitative path-measure quantifier and the undecidability of the emptiness problem for alternating tree automata with non-zero semantics, a recently introduced probabilistic model of alternating tree automata.

We introduce an extension of Strategy Logic for the imperfect-information setting, called SL ii and study its model-checking problem. As this logic naturally captures multi-player games with imperfect information, this problem is undecidable; but we introduce a syntactical class of “hierarchical instances” for which, intuitively, as one goes down the syntactic tree of the formula, strategy quantifications are concerned with finer observations of the model, and we prove that model-checking SL ii restricted to hierarchical instances is decidable. This result, because it allows for complex patterns of existential and universal quantification on strategies, greatly generalises the decidability of distributed synthesis for systems with hierarchical information. It allows us to easily derive new decidability results concerning strategic problems under imperfect information such as the existence of Nash equilibria or rational synthesis. To establish this result, we go through an intermediary, “low-level” logic much more adapted to automata techniques. QCTL * is an extension of CTL * with second-order quantification over atomic propositions that has been used to study strategic logics with perfect information. We extend it to the imperfect information setting by parameterising second-order quantifiers with observations. The simple syntax of the resulting logic, QCTL * ii , allows us to provide a conceptually neat reduction of SL ii to QCTL * ii that separates concerns, allowing one to forget about strategies and players and focus solely on second-order quantification. While the model-checking problem of QCTL * ii is, in general, undecidable, we identify a syntactic fragment of hierarchical formulas and prove, using an automata-theoretic approach, that it is decidable.

We consider an agent that operates with two models of the environment: one that captures expected behaviors and one that captures additional exceptional behaviors. We study the problem of synthesizing agent strategies that enforce a goal against environments operating as expected while also making a best effort against exceptional environment behaviors. We formalize these concepts in the context of linear-temporal logic, and give an algorithm for solving this problem. We also show that there is no trade-off between enforcing the goal under the expected environment specification and making a best-effort for it under the exceptional one.

We review PLTLf and PLDLf, the pure-past versions of the well-known logics on finite traces LTLf and LDLf, respectively. PLTLf and PLDLf are logics about the past, and so scan the trace backwards from the end towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, over LTLf /LDLf , for computing the corresponding DFA. This exponential improvement is reflected in several forms of sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp., PLDLf ) has the same expressive power as LTLf (resp., LDLf ), but transforming a PLTLf (resp., PLDLf ) formula into its equivalent LTLf (resp.,LDLf) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLDLf .

We look at program synthesis where the aim is to automatically synthesize a controller that operates on data structures and from which a concrete program can be easily derived. We do not aim at a fully-automatic process or tool that produces a program meeting a given specification of the program’s behaviour. Rather, we aim at the design of a clear and well-founded approach for supporting programmers at the design and implementation phases. Concretely, we first show that a program synthesis task can be modeled as a generalized planning problem. This is done at an abstraction level where the involved data structures are seen as black-boxes that can be interfaced with actions and observations, the first corresponding to the operations and the second to the queries provided by the data structure. The abstraction level is high enough to capture intuitive and common assumptions as well as general and simple strategies used by programmers, and yet it contains sufficient structure to support the automated generation of concrete solutions (in the form of controllers). From such controllers and the use of standard data structures, an actual program in a general language like C++ or Python can be easily obtained. Then, we discuss how the resulting generalized planning problem can be reduced to an LTL synthesis problem, thus making available any LTL synthesis engine for obtaining the controllers. We illustrate the effectiveness of the approach on a series of examples.

We introduce Probabilistic Strategy Logic, an extension of Strategy Logic for stochastic systems. The logic has probabilistic terms that allow it to express many standard solution concepts, such as Nash equilibria in randomised strategies, as well as constraints on probabilities, such as independence. We study the model-checking problem for agents with perfect- and imperfect-recall. The former is undecidable, while the latter is decidable in space exponential in the system and triple-exponential in the formula. We identify a natural fragment of the logic, in which every temporal operator is immediately preceded by a probabilistic operator, and show that it is decidable in space exponential in the system and the formula, and double-exponential in the nesting depth of the probabilistic terms. Taking a fixed nesting depth, this gives a fragment that still captures many standard solution concepts, and is decidable in exponential space.

Program synthesis constructs programs from specifications in an automated way. Strategy Logic (SL) is a powerful and versatile specification language whose goal is to give theoretical foundations for program synthesis in a multi-agent setting. One limitation of Strategy Logic is that it is purely qualitative. For instance it cannot specify quantitative properties of executions such as "every request is quickly granted", or quantitative properties of trees such as "most executions of the system terminate". In this work, we extend Strategy Logic to include quantitative aspects in a way that can express bounds on "how quickly" and "how many". We define Prompt Strategy Logic, which encompasses Prompt LTL (itself an extension of LTL with a prompt eventuality temporal operator), and we define Bounded-Outcome Strategy Logic which has a bounded quantifier on paths. We supply a general technique, based on the study of automata with counters, that solves the model-checking problems for both these logics.

We develop a logic-based technique to analyse finite interactions in multi-agent systems. We introduce a semantics for Alternating-time Temporal Logic (for both perfect and imperfect recall) and its branching-time fragments in which paths are finite instead of infinite. We study validities of these logics and present optimal algorithms for their model-checking problems in the perfect recall case.

We study planning for LTLf and LDLf temporally extended goals in nondeterministic fully observable domains (FOND). We consider both strong and strong cyclic plans, and develop foundational automata-based techniques to deal with both cases. Using these techniques we provide the computational characterization of both problems, separating the complexity in the size of the domain specification from that in the size of the formula. Specifically we establish them to be EXPTIME-complete and 2EXPTIME-complete, respectively, for both problems. In doing so, we also show 2EXPTIME-hardness for strong cyclic plans, which was open.

Parameterized model checking is the problem of deciding if a given formula holds irrespective of the number of participating processes. A standard approach for solving the parameterized model checking problem is to reduce it to model checking finitely many finite-state systems. This work considers the theoretical power and limitations of this technique. We focus on concurrent systems in which processes communicate via pairwise rendezvous, as well as the special cases of disjunctive guards and token passing; specifications are expressed in indexed temporal logic without the next operator; and the underlying network topologies are generated by suitable formulas and graph operations. First, we settle the exact computational complexity of the parameterized model checking problem for some of our concurrent systems, and establish new decidability results for others. Second, we consider the cases where model checking the parameterized system can be reduced to model checking some fixed number of processes, the number is known as a cutoff. We provide many cases for when such cutoffs can be computed, establish lower bounds on the size of such cutoffs, and identify cases where no cutoff exists. Third, we consider cases for which the parameterized system is equivalent to a single finite-state system (more precisely a Büchi word automaton), and establish tight bounds on the sizes of such automata.

We study concurrent games with finite-memory strategies where players are given a Buchi and a mean-payoff objective, which are related by a lexicographic order: a player first prefers to satisfy its Buchi objective, and then prefers to minimise costs, which are given by a mean-payoff function. In particular, we show that deciding the existence of a strict Nash equilibrium in such games is decidable, even if players' deviations are implemented as infinite memory strategies.

We study a class of synchronous, perfect-recall multi-agent systemswith imperfect information and broadcasting (i.e., fully observableactions). We define an epistemic extension of strategy logic withincomplete information and the assumption of uniform and coherentstrategies. In this setting, we prove that the model checking problem,and thus rational synthesis, is decidable with non-elementarycomplexity. We exemplify the applicability of the framework on arational secret-sharing scenario.

We study the characterization and computation of general policies for families of problems that share a structure characterized by a common reduction into a single abstract problem. Policies mu that solve the abstract problem P have been shown to solve all problems Q that reduce to P provided that mu terminates in Q. In this work, we shed light on why this termination condition is needed and how it can be removed. The key observation is that the abstract problem P captures the common structure among the concrete problems Q that is local (Markovian) but misses common structure that is global. We show how such global structure can be captured by means of trajectory constraints that in many cases can be expressed as LTL formulas, thus reducing generalized planning to LTL synthesis. Moreover, for a broad class of problems that involve integer variables that can be increased or decreased, trajectory constraints can be compiled away, reducing generalized planning to fully observable non-deterministic planning.

We introduce an extension of Strategy logic for the imperfect-information setting, called SLii, and study its model-checking problem. As this logic naturally captures multi-player games with imperfect information, the problem turns out to be undecidable. We introduce a syntactical class of “hierarchical instances” for which, intuitively, as one goes down the syntactic tree of the formula, strategy quantifications are concerned with finer observations of the model. We prove that model-checking SLiirestricted to hierarchical instances is decidable. This result, because it allows for complex patterns of existential and universal quantification on strategies, greatly generalises previous ones, such as decidability of multi-player games with imperfect information and hierarchical observations, and decidability of distributed synthesis for hierarchical systems. To establish the decidability result, we introduce and study QCTLii*, an extension of QCTL (itself an extension of CTL with second-order quantification over atomic propositions) by parameterising its quantifiers with observations. The simple syntax of QCTLii* allows us to provide a conceptually neat reduction of SLiito QCTLii* that separates concerns, allowing one to forget about strategies and players and focus solely on second-order quantification. While the model-checking problem of QCTLii* is, in general, undecidable, we identify a syntactic fragment of hierarchical formulas and prove, using an automata-theoretic approach, that it is decidable. The decidability result for SLiifollows since the reduction maps hierarchical instances of SLiito hierarchical formulas of QCTLii*.

We study the expressive power of logical interpretations on the class of scattered trees, namely those with countably many infinite branches. Scattered trees can be thought of as the tree analogue of scattered linear orders. Every scattered tree has an ordinal rank that reflects the structure of its infinite branches. We prove, roughly, that trees and orders of large rank cannot be interpreted in scattered trees of small rank. We consider a quite general notion of interpretation: each element of the interpreted structure is represented by a set of tuples of subsets of the interpreting tree. Our trees are countable, not necessarily finitely branching, and may have finitely many unary predicates as labellings. We also show how to replace injective set-interpretations in (not necessarily scattered) trees by âfinitary' set-interpretations.

We compare the expressiveness of two extensions of monadic second-order logic (MSO) over the class of finite structures. The first, counting monadic second-order logic (CMSO), extends MSO with first-order modulo-counting quantifiers, allowing the expression of queries like ``the number of elements in the structure is even''. The second extension allows the use of an additional binary predicate, not contained in the signature of the queried structure, that must be interpreted as an arbitrary linear order on its universe, obtaining order-invariant MSO. While it is straightforward that every CMSO formula can be translated into an equivalent order-invariant MSO formula, the converse had not yet been settled. Courcelle showed that for restricted classes of structures both order-invariant MSO and CMSO are equally expressive, but conjectured that, in general, order-invariant MSO is stronger than CMSO. We affirm this conjecture by presenting a class of structures that is order-invariantly definable in MSO but not definable in CMSO.

We investigate structures that can be represented by omega-automata, so called omega-automatic structures, and prove that relations defined over such structures in first-order logic expanded by the first-order quantifiers `there exist at most $\aleph_0$ many', 'there exist finitely many' and 'there exist $k$ modulo $m$ many' are omega-regular. The proof identifies certain algebraic properties of omega-semigroups. As a consequence an omega-regular equivalence relation of countable index has an omega-regular set of representatives. This implies Blumensath's conjecture that a countable structure with an $ω$-automatic presentation can be represented using automata on finite words. This also complements a very recent result of Hjörth, Khoussainov, Montalban and Nies showing that there is an omega-automatic structure which has no injective presentation.

We study the existence of automatic presentations for various algebraic structures. An automatic presentation of a structure is a description of the universe of the structure by a regular set of words, and the interpretation of the relations by synchronised automata. Our first topic concerns characterising classes of automatic structures. We supply a characterisation of the automatic Boolean algebras, and it is proven that the free Abelian group of infinite rank, as well as certain Fraisse limits, do not have automatic presentations. In particular, the countably infinite random graph and the random partial order do not have automatic presentations. Furthermore, no infinite integral domain is automatic. Our second topic is the isomorphism problem. We prove that the complexity of the isomorphism problem for the class of all automatic structures is \Sigma_1^1-complete.

We investigate partial orders that are computable, in a precise sense, by finite automata. Our emphasis is on trees and linear orders. We study the relationship between automatic linear orders and trees in terms of rank functions that are related to Cantor--Bendixson rank. We prove that automatic linear orders and automatic trees have finite rank. As an application we provide a procedure for deciding the isomorphism problem for automatic ordinals. We also investigate the complexity and definability of infinite paths in automatic trees. In particular, we show that every infinite path in an automatic tree with countably many infinite paths is a regular language.

This paper studies the existence of automatic presentations for various algebraic structures. The automatic Boolean algebras are characterised, and it is proven that the free Abelian group of infinite rank and many Fraisse limits do not have automatic presentations. In particular, the countably infinite random graph and the universal partial order do not have automatic presentations. Furthermore, no infinite integral domain is automatic. The second topic of the paper is the isomorphism problem. We prove that the complexity of the isomorphism problem for the class of all automatic structures is /spl Sigma//sub 1//sup 1/-complete.

We investigate partial orders that are computable, in a precise sense, by finite automata. Our emphasis is on trees and linear orders. We study the relationship between automatic linear orders and trees in terms of rank functions that are versions of Cantor-Bendixson rank. We prove that automatic linear orders and automatic trees have finite rank. As an application we provide a procedure for deciding the isomorphism problem for automatic ordinals. We also investigate the complexity and definability of infinite paths in automatic trees. In particular, we show that every infinite path in an automatic tree with countably many infinite paths is a regular language.

We study the class of countable structures which can be presented by synchronous finite automata. We reduce the problem of existence of an automatic presentation of a structure to that for a graph. We exhibit a series of properties of automatic equivalence structures, linearly ordered sets and permutation structures. These serve as a first step in producing practical descriptions of some automatic structures or illuminating the complexity of doing so for others.