We provide a new algebraic technique to solve the sequential flow problem in polynomial space. The task is to maximise the flow through a graph where edge capacities can be changed over time by choosing a sequence of capacity labelings from a given finite set. Our method is based on a novel factorization theorem for finite semigroups that, applied to a suitable flow semigroup, allows to derive small witnesses. This generalises to multiple in/output vertices, as well as regular constraints.

The population control problem is a parameterised problem where a controller sends messages to a whole population of identical finite-state agents, aiming to eventually move them all into a target state. The decision problem asks whether this can be achieved for arbitrarily large finite populations. We focus on the randomised version of this problem, where every agent is a copy of the same finite Markov Decision Process and non-determinism in the global action chosen by the controller is resolved independently and uniformly at random. Colcombet, Fijalkow and Ohlmann [Thomas Colcombet et al., 2021] showed that this problem is decidable, but without any complexity upper bound. We show that the random population control problem is in fact ExpTime-complete.

Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied problem is known to be undecidable already for very simple omega-regular objectives, because of the difficulty of reasoning on uncertain events. We introduce a revelation mechanism which restricts information loss by requiring that almost surely the agent has eventually full information of the current state. Our main technical results are to construct exact algorithms for two classes of POMDPs called weakly and strongly revealing. Importantly, the decidable cases reduce to the analysis of a finite belief-support Markov decision process. This yields a conceptually simple and exact algorithm for a large class of POMDPs.

For a fixed alphabet A, an infinite sequence X is said to be normal if every word w over A appears in X with the same frequency as any other word of the same length. A classical result of Agafonov (1966) relates normality to finite automata as follows: a sequence X is normal if and only if any subsequence of X selected by a finite automaton is itself normal. Another theorem of Schnorr and Stimm (1972) gives an alternative characterization: a sequence X is normal if and only if no gambler can win large amounts of money by betting on the sequence X using a strategy that can be described by a finite automaton. Both of these theorems are established in the setting of deterministic finite automata. This raises the question as to whether they can be extended to the setting of probabilistic finite automata. In the case of the Agafonov theorem, a partial positive answer was given by Léchine et al. (MFCS 2024) in a restricted case of probabilistic automata with rational transition probabilities. In this paper, we settle the full conjecture by proving that both the Agafonov and the Schnorr-Stimm theorems hold true for arbitrary probabilistic automata. Specifically, we show that a sequence X is normal if and only if any probabilistic automaton selects a normal subsequence of X with probability 1 and also show that a sequence X is normal if and only if any probabilistic finite-state gambler fails to win on X with probability 1.

We consider the distributed control synthesis problem for systems with locks. The goal is to find local controllers so that the global system does not deadlock. With no restriction this problem is undecidable even for three processes each using a fixed number of locks. We propose two restrictions that make distributed control decidable. The first one is to allow each process to use at most two locks. The problem then becomes $Σ_2^P$-complete, and even in PTIME under some additional assumptions. The dining philosophers problem satisfies these assumptions. The second restriction is a nested usage of locks. In this case the synthesis problem is NEXPTIME-complete. The drinking philosophers problem falls in this case.

We consider the distributed control synthesis problem for systems with locks. The goal is to find local controllers so that the global system does not deadlock. With no restriction this problem is undecidable even for three processes each using a fixed number of locks. We propose two restrictions that make distributed control decidable. The first one is to allow each process to use at most two locks. The problem then becomes complete for the second level of the polynomial time hierarchy, and even in Ptime under some additional assumptions. The dining philosophers problem satisfies these assumptions. The second restriction is a nested usage of locks. In this case the synthesis problem is Nexptime-complete. The drinking philosophers problem falls in this case.

We show the undecidability of the distributed control problem when the plant is an asynchronous automaton, the controllers use causal memory and the goal of the controllers is to put each process in a local accepting state.

Stable matching in a community consisting of men and women is a classical combinatorial problem that has been the subject of intense theoretical and empirical study since its introduction in 1962 in a seminal paper by Gale and Shapley, who designed the celebrated ``deferred acceptance'' algorithm for the problem. In the input, each participant ranks participants of the opposite type, so the input consists of a collection of permutations, representing the preference lists. A bipartite matching is unstable if some man-woman pair is blocking: both strictly prefer each other to their partner in the matching. Stability is an important economics concept in matching markets from the viewpoint of manipulability. The unicity of a stable matching implies non-manipulability, and near-unicity implies limited manipulability, thus these are mathematical properties related to the quality of stable matching algorithms. This paper is a theoretical study of the effect of correlations on approximate manipulability of stable matching algorithms. Our approach is to go beyond worst case, assuming that some of the input preference lists are drawn from a distribution. Our model encompasses a discrete probabilistic process inspired by a popularity model introduced by Immorlica and Mahdian, that provides a way to capture correlation between preference lists. Approximate manipulability is approached from several angles : when all stable partners of a person have approximately the same rank; or when most persons have a unique stable partner. Another quantity of interest is a person's number of stable partners. Our results aim to paint a picture of the manipulability of stable matchings in a ``beyond worst case'' setting.

We introduce a new setting where a population of agents, each modelled by a finite-state system, are controlled uniformly: the controller applies the same action to every agent. The framework is largely inspired by the control of a biological system, namely a population of yeasts, where the controller may only change the environment common to all cells. We study a synchronisation problem for such populations: no matter how individual agents react to the actions of the controller, the controller aims at driving all agents synchronously to a target state. The agents are naturally represented by a non-deterministic finite state automaton (NFA), the same for every agent, and the whole system is encoded as a 2-player game. The first player (Controller) chooses actions, and the second player (Agents) resolves non-determinism for each agent. The game with m agents is called the m -population game. This gives rise to a parameterized control problem (where control refers to 2 player games), namely the population control problem: can Controller control the m-population game for all m in N whatever Agents does? Comment: This is a journal version of the extended abstract arXiv:1707.02058 which appeared in Concur 2017, together with proofs

We introduce a new class of automata on infinite trees called alternating nonzero automata, which extends the class of non-deterministic nonzero automata. The emptiness problem for this class is still open, however we identify a subclass, namely limited choice, for which we reduce the emptiness problem for alternating nonzero automata to the same problem for non-deterministic ones, which implies decidability. We obtain, as corollaries, algorithms for the satisfiability of a probabilistic temporal logic extending both CTL* and the qualitative fragment of pCTL*.

The decidability of the distributed version of the Ramadge and Wonham controller synthesis problem, where both the plant and the controllers are modeled as asynchronous automata and the controllers have causal memory is a challenging open problem. There exist three classes of plants for which the existence of a correct controller with causal memory has been shown decidable: when the dependency graph of actions is series-parallel, when the processes are connectedly communicating and when the dependency graph of processes is a tree. We design a class of plants, called decomposable games, with a decidable controller synthesis problem. This provides a unified proof of the three existing decidability results as well as new examples of decidable plants.

Zero automata are a probabilistic extension of parity automata on infinite trees. The satisfiability of a certain probabilistic variant of MSO, called TMSO+zero, reduces to the emptiness problem for zero automata. We introduce a variant of zero automata called nonzero automata. We prove that for every zero automaton there is an equivalent nonzero automaton of quadratic size and the emptiness problem of nonzero automata is decidable, with complexity co-NP. These results imply that TMSO+zero has decidable satisfiability.

We consider two-person zero-sum stochastic games with signals, a standard model of stochastic games with imperfect information. The only source of information for the players consists of the signals they receive; they cannot directly observe the state of the game, nor the actions played by their opponent, nor their own actions. We are interested in the existence of almost-surely winning or positively winning strategies, under reachability, safety, Büchi, or co-Büchi winning objectives, and the computation of these strategies when the game has finitely many states and actions. We prove two qualitative determinacy results. First, in a reachability game, either player 1 can achieve almost surely the reachability objective, or player 2 can achieve surely the dual safety objective, or both players have positively winning strategies. Second, in a Büchi game, if player 1 cannot achieve almost surely the Büchi objective, then player 2 can ensure positively the dual co-Büchi objective. We prove that players only need strategies with finite memory . The number of memory states needed to win with finite-memory strategies ranges from one (corresponding to memoryless strategies) to doubly exponential, with matching upper and lower bounds. Together with the qualitative determinacy results, we also provide fix-point algorithms for deciding which player has an almost-surely winning or a positively winning strategy and for computing an associated finite-memory strategy. Complexity ranges from EXPTIME to 2EXPTIME, with matching lower bounds. Our fix-point algorithms also enjoy a better complexity in the cases where one of the players is better informed than their opponent. Our results hold even when players do not necessarily observe their own actions. The adequate class of strategies, in this case, is mixed or general strategies (they are equivalent). Behavioral strategies are too restrictive to guarantee determinacy: it may happen that one of the players has a winning general strategy but none of them has a winning behavioral strategy. On the other hand, if a player can observe their actions, then general, mixed, and behavioral strategies are equivalent. Finite-memory strategies are sufficient for determinacy to hold, provided that randomized memory updates are allowed.

This paper reports about the robotic installation set up by the Rhoban Project in the French pavilion of the Expo 2012 of Yeosu, Korea ([6]). The installation has consisted in a humorous show involving humanoid robots and anthropomorphic arms, with the illusion of life as a guideline. We emphasized natural compliant motion and physical interaction in order to make the show attractive. The design raised some issues dealing with robustness of robots, but also with the realism of the motions and the synchronization of the robots with the music.

The value 1 problem is a decision problem for probabilistic automata over finite words: given a probabilistic automaton, are there words accepted with probability arbitrarily close to 1? This problem was proved undecidable recently. We sharpen this result, showing that the undecidability holds even if the probabilistic automata have only one probabilistic transition. Our main contribution is to introduce a new class of probabilistic automata, called leaktight automata, for which the value 1 problem is shown decidable (and PSPACE-complete). We construct an algorithm based on the computation of a monoid abstracting the behaviors of the automaton, and rely on algebraic techniques developed by Simon for the correctness proof. The class of leaktight automata is decidable in PSPACE, subsumes all subclasses of probabilistic automata whose value 1 problem is known to be decidable (in particular deterministic automata), and is closed under two natural composition operators.

Infinite stochastic games are a natural model for open reactive processes: one player represents the controller and the other represents a hostile environment. The evolution of the system depends on the decisions of the players, supplemented by chance. There are two main algorithmic problems on such games: computing the values of the vertices (quantitative analysis) and deciding whether a player can win with probability one, or arbitrarily close to one (qualitative analysis). In this paper, we reduce the quantitative analysis of simple stochastic tail games (where both players have perfect information and the winner does not depend on finite prefixes) to the qualitative analysis: we provide an algorithm computing values which uses qualitative analysis sub-procedure. The correctness proof of this algorithm reveals several nice properties of perfect-information stochastic tail games, in particular the existence of optimal strategies. We apply these results to games whose winning conditions are boolean combinations of mean-payoff and Büchi conditions.

We consider the standard model of finite two person zero sum stochastic games with signals. We are interested in the existence of almost surely winning or positively winning strategies, under reachability, safety, Buchi or co-Buchi winning objectives. We prove two qualitative determinacy results. First, in a reachability game either player 1 can achieve almost-surely the reachability objective, or player 2 can ensure surely the complementary safety objective, or both players have positively winning strategies. Second, in a Buchi game if player 1 cannot achieve almost-surely the Buchi objective, then player 2 can ensure positively the complementary co-Buchi objective. We prove that players only need strategies with finite memory, whose sizes range from no memory at all to doubly-exponential number of states, with matching lower bounds. Together with the qualitative determinacy results, we also provide fix point algorithms for deciding which player has an almost surely winning or a positively winning strategy and for computing the finite memory strategy. Complexity ranges from EXPTIME to 2-EXPTIME with matching lower bounds, and better complexity can be achieved for some special cases where one of the players is better informed than her opponent.

Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. The payoff received by the controller can be evaluated in different ways, depending on the payoff function the MDP is equipped with. For example a mean-payoff function evaluates average performance, whereas a discounted payoff function gives more weights to earlier performance by means of a discount factor. Another well-known example is the parity payoff function which is used to encode logical specifications. Surprisingly, parity and mean-payoff MDPs share two non-trivial properties: they both have pure stationary optimal strategies and they both are approximable by discounted MDPs with multiple discount factors (multi- discounted MDPs). In this paper we unify and generalize these results. We introduce a new class of payoff functions called the priority weighted payoff functions, which are generalization of both parity and mean-payoff functions. We prove that priority weighted MDPs admit optimal strategies that are pure and stationary, and that the priority weighted value of an MDP is the limit of the multi-discounted value when discount factors tend to 0 simultaneously at various speeds.