86 research outputs found
The problem with probabilistic DAG automata for semantic graphs
Semantic representations in the form of directed acyclic graphs (DAGs) have
been introduced in recent years, and to model them, we need probabilistic
models of DAGs. One model that has attracted some attention is the DAG
automaton, but it has not been studied as a probabilistic model. We show that
some DAG automata cannot be made into useful probabilistic models by the nearly
universal strategy of assigning weights to transitions. The problem affects
single-rooted, multi-rooted, and unbounded-degree variants of DAG automata, and
appears to be pervasive. It does not affect planar variants, but these are
problematic for other reasons.Comment: To appear in NAACL-HLT 201
Computing the Longest Common Prefix of a Context-free Language in Polynomial Time
We present two structural results concerning the longest common prefixes of non-empty languages.
First, we show that the longest common prefix of the language generated by a context-free grammar of size N
equals the longest common prefix of the same grammar where the heights of the derivation trees are bounded by
4N.
Second, we show that each non-empty language L has a representative subset of at most three elements which behaves
like L w.r.t. the longest common prefix as well as w.r.t. longest common prefixes of L after unions or
concatenations with arbitrary other languages.
From that, we conclude
that the longest common prefix, and thus the longest common suffix, of a context-free language can be computed in polynomial time
Extending Finite-Memory Determinacy by Boolean Combination of Winning Conditions
We study finite-memory (FM) determinacy in games on finite graphs, a central question for applications in controller synthesis, as FM strategies correspond to implementable controllers. We establish general conditions under which FM strategies suffice to play optimally, even in a broad multi-objective setting. We show that our framework encompasses important classes of games from the literature, and permits to go further, using a unified approach. While such an approach cannot match ad-hoc proofs with regard to tightness of memory bounds, it has two advantages: first, it gives a widely-applicable criterion for FM determinacy; second, it helps to understand the cornerstones of FM determinacy, which are often hidden but common in proofs for specific (combinations of) winning conditions
The Impatient May Use Limited Optimism to Minimize Regret
Discounted-sum games provide a formal model for the study of reinforcement
learning, where the agent is enticed to get rewards early since later rewards
are discounted. When the agent interacts with the environment, she may regret
her actions, realizing that a previous choice was suboptimal given the behavior
of the environment. The main contribution of this paper is a PSPACE algorithm
for computing the minimum possible regret of a given game. To this end, several
results of independent interest are shown. (1) We identify a class of
regret-minimizing and admissible strategies that first assume that the
environment is collaborating, then assume it is adversarial---the precise
timing of the switch is key here. (2) Disregarding the computational cost of
numerical analysis, we provide an NP algorithm that checks that the regret
entailed by a given time-switching strategy exceeds a given value. (3) We show
that determining whether a strategy minimizes regret is decidable in PSPACE
Stackelberg-Pareto Synthesis
In this paper, we study the framework of two-player Stackelberg games played on graphs in which Player 0 announces a strategy and Player 1 responds rationally with a strategy that is an optimal response. While it is usually assumed that Player 1 has a single objective, we consider here the new setting where he has several. In this context, after responding with his strategy, Player 1 gets a payoff in the form of a vector of Booleans corresponding to his satisfied objectives. Rationality of Player 1 is encoded by the fact that his response must produce a Pareto-optimal payoff given the strategy of Player 0. We study the Stackelberg-Pareto Synthesis problem which asks whether Player 0 can announce a strategy which satisfies his objective, whatever the rational response of Player 1. For games in which objectives are either all parity or all reachability objectives, we show that this problem is fixed-parameter tractable and NEXPTIME-complete. This problem is already NP-complete in the simple case of reachability objectives and graphs that are trees
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