25.9 Reachability in Low-Dimensional VASS

A \(d\)-dimensional Vector Addition System with States (\(d\)-VASS) is a finite automaton equipped with a finite set of states \(\mathrm{Q}\) and \(d\) non-negative counters. Each transition updates the counters by adding a vector \(v \in \mathbb{Z}^d\) coordinate-wise, provided that no counter becomes negative in the process. The reachability problem asks whether a run exists from a given source configuration \(s \in \mathrm{Q} \times \mathbb{N}^d\) to a target configuration \(t \in \mathrm{Q} \times \mathbb{N}^d\). It is known that reachability is TOWER-hard when \(d = 8\). An open question is whether this TOWER-hardness result can be achieved in a lower dimension. \(\\\)

Recently, significant attention has been given to the geometric dimension of VASS, defined as the dimension of the vector space spanned by the effects of all cycles. As an intermediate step, one may ask whether the TOWER-hardness result can be established for VASS of lower geometric dimension.

25.8 Semilinearity of 2-BVASS backward reachability set

A \(d\)-dimensional Vector Addition System with States (VASS) is a finite automaton \(V = (Q, \Delta)\) with transitions labeled by integer vectors: \(\Delta \subseteq Q \times \mathbb{Z}^d \times Q\). Its set of configurations is \(Q \times \mathbb{N}^d\). An execution of \(V\) is a sequence of configurations \[ (q_0, \vec x_0) \to (q_1, \vec x_1) \to \cdots \to (q_n, \vec x_n) \] such that for each \(i < n\), there exists a transition \((q_i, \vec v_i, q_{i+1}) \in \Delta\) satisfying \(\vec x_{i+1} = \vec x_i + \vec v_i \in \mathbb{N}^d\).

The reachability relation \(R_V \subseteq (Q \times \mathbb{N}^d) \times (Q \times \mathbb{N}^d)\) is the set of pairs of configurations connected by an execution. The reachability set of \(V\) from an initial configuration \((q_{\text{init}}, \vec x_{\text{init}})\) is \[ \{ (q, \vec x) \mid ((q_{\text{init}}, \vec x_{\text{init}}), (q, \vec x)) \in R_V \}, \] and the backward reachability set from a final configuration \((q_{\text{final}}, \vec x_{\text{final}})\) is \[ \{ (q, \vec x) \mid ((q, \vec x), (q_{\text{final}}, \vec x_{\text{final}})) \in R_V \}. \]

A Branching VASS (BVASS) \(B = (Q, \Delta_1, \Delta_2)\) is a VASS \((Q, \Delta_1)\) extended with a second kind of transitions, called branching transitions, encoded as triples of states \((p, q, r) \in \Delta_2\). Executions of \(B\) are finite trees labeled by configurations, constructed bottom-up. All leaves are labeled by the same initial configuration, and every internal node is obtained either from a single child by applying a VASS transition from \(\Delta_1\), or from two children via a branching transition. In the latter case, there exists \((p, q, r) \in \Delta_2\) such that the children are in control states \(p\) and \(q\), the parent is in state \(r\), and the vector of the parent is the sum of the vectors of the two children. The final configuration of this run is the configuration of the root. The reachability relation and reachability sets are defined analogously to the case of VASS.\(\\\)

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25.7 Strong subgame perfect equilibria

A \(k\)-player (turn-based) game is given by a directed graph \(G = (V, E)\) with each vertex owned by one of the \(k\) players, and an objective \(W_i \subseteq V^\omega\) for each player \(i\). A strategy profile \(\bar{\sigma} = (\sigma_1, \ldots, \sigma_k)\) is a tuple of strategies, one for each player. A strategy profile is Nash equilibrium (NE) if no player can benefit by unilaterally changing their strategy, i.e., if \(\bar{\sigma}\) produces a losing outcome for player \(i\), then Player \(i\) cannot deviate to a different strategy \(\sigma_i'\) that would produce a winning outcome. A strategy profile is a subgame perfect equilibrium (SPE) if it is an NE in every possible subgame of the original game. It is well-known that SPE is a more appropriate notion in this setting and well-studied for \(k\)-player games with \(\omega\)-regular objectives.\(\\\)

A more robust notion of NE is strong Nash equilibrium (SNE), where no coalition of players can cooperatively deviate in a way that strictly benefits all of its members. Based on SNE, we consider the notion of strong subgame perfect equilibrium (SSPE), which is a strategy profile that is SNE in every subgame.\(\\\)

Question:\(\\\)

  1. Does it always exists an SSPE in \(k\)-player games with \(\omega\)-regular objectives?\(\\\)
  2. If yes, can we compute a maximal SSPE?

25.6 Fine-grained complexity of language emptiness for deterministic PDA

\(\textbf{Input: }\) A deterministic pushdown automaton \(\mathcal{P}\), with \(n\) states and constantly many input and stack letters. \(\\ \) \( \textbf{Output:}\) Yes, if \(\mathcal{L(P)}\) is empty. No, otherwise. \(\\ \) \(\textbf{Question: }\) What is the fine-grained complexity of this problem? Can we obtain an improved upper bound \((\mathcal{O}(n^{3-\epsilon})\) or an improved conditional lower bound \((\Omega(n^{2 + \epsilon}))\) for this problem? \(\\ \)

\(\textbf{Context: }\)The best known upper bound for this problem seems to be \(\mathcal{O}(n^3)\) time by using the standard method for solving pushdown emptiness. The best known fine-grained reduction seems to be from the Orthogonal Vectors conjecture or the Strong Exponential Time Hypothesis, either of which gives a quadratic lower bound. Other existing fine-grained reductions from pushdown automata emptiness to hard problems like triangle detection, clique detection, or NFA acceptance all seem to require nondeterminism. These reductions can be determinized by using an \(\mathcal{O}(n)\) sized input alphabet, but encoding this alphabet in binary can result in an \(\mathcal{O}(n^2)\) sized state set. It is not obvious how to avoid this blowup, but the problem already seems hard for a constant-sized alphabet.

25.5 Commutative polynomial automata series

A series \(f : \Sigma^* \to \mathbb Q\) is commutative if the output does not depend on the order of the symbols in the input. Given a polynomial automaton recognising a commutative series, does there exist an equivalent polynomial automaton where all control states recognise commutative series? The answer for weighted automata is yes.

25.4 Efficient LRS evaluation

Consider a linear recurrence sequence \(f : \mathbb N \to \mathbb Q\). Given an input index \(n \in \mathbb N\) in binary encoding, decide whether \(f(n) = 0\). Can this be done in polynomial time?

25.3 Relative degree for rational series

Consider a two letter alphabet \(\Sigma = \{a, b\}\). A formal series \(f : \Sigma^* \to \mathbb Q\) has relative degree \(r \in \mathbb N\) if

  1. all words \(w \in \Sigma^*\) in its support \(f(w) \neq 0\) are of the form \(w \in a^r \cdot \Sigma^* \), and
  2. \(f(a^r \cdot b) \neq 0\).

Decide whether a rational series has a relative degree.

25.2 Permissive strategies for reachability games

A strategy in a two player game on graphs is said to be "permissive" if it is winning and it allows all behaviours of all winning memoryless strategies. For safety objectives, there is a most permissive strategy, and this most permissive strategy is in fact memoryless [Bernet, Janin, Walukiewicz'2002]. On the other hand, for reachability objectives, permissive strategies require memory.\(\\\)

What is the complexity of the following problem:\(\\\)

Given a reachability game and a constant c, does there exist a permissive strategy with memory at most c?\(\\\)

It can be assumed that the constant c is at most the size of the game. So its encoding does not matter.

25.1 Complexity of generalised reachability games with target sets of size 2

Fijalkow and Horn studied generalised reachability games on graphs, which requires Player 1 to visit at least one vertex from each given target set. In general, this problem is PSPACE-complete, even if all target sets are of size 3. Furthermore, if all target sets are of size 1, the problem is solvable in PTIME. However when all target sets are of size at most 2, the complexity is currently unknown. Hence, the best known upper bound is PSPACE and the best known lower bound is PTIME. Can we obtain better upper or lower bounds for this case?

24.19 Reachability in Reversible Data VASS

Is the reachability problem decidable for reversible data VAS where the set of data values A satisfy the following properties:

  1. A is a homogeneous relational structure and has a linear order as one of its relations.
  2. The embedding relation on finite substructures of X labelled with natural numbers is a well quasi order.

Some explanations: By saying A is a homogeneous relational structure we mean that any isomorphism between two finite substructures of A can be extended to an automorphism of A.

Examples: Dense linear order, random/Rado graph.

This question comes from the following paper with Sławomir Lasota: arXiv - LICS'24 proceedings link