24 research outputs found
Symmetric Strategy Improvement
Symmetry is inherent in the definition of most of the two-player zero-sum
games, including parity, mean-payoff, and discounted-payoff games. It is
therefore quite surprising that no symmetric analysis techniques for these
games exist. We develop a novel symmetric strategy improvement algorithm where,
in each iteration, the strategies of both players are improved simultaneously.
We show that symmetric strategy improvement defies Friedmann's traps, which
shook the belief in the potential of classic strategy improvement to be
polynomial
Permutation Games for the Weakly Aconjunctive -Calculus
We introduce a natural notion of limit-deterministic parity automata and
present a method that uses such automata to construct satisfiability games for
the weakly aconjunctive fragment of the -calculus. To this end we devise a
method that determinizes limit-deterministic parity automata of size with
priorities through limit-deterministic B\"uchi automata to deterministic
parity automata of size and with
priorities. The construction relies on limit-determinism to avoid the full
complexity of the Safra/Piterman-construction by using partial permutations of
states in place of Safra-Trees. By showing that limit-deterministic parity
automata can be used to recognize unsuccessful branches in pre-tableaux for the
weakly aconjunctive -calculus, we obtain satisfiability games of size
with priorities for weakly aconjunctive
input formulas of size and alternation-depth . A prototypical
implementation that employs a tableau-based global caching algorithm to solve
these games on-the-fly shows promising initial results
Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis
We propose "semantic labelling" as a novel ingredient for solving games in
the context of LTL synthesis. It exploits recent advances in the automata-based
approach, yielding more information for each state of the generated parity game
than the game graph can capture. We utilize this extra information to improve
standard approaches as follows. (i) Compared to strategy improvement (SI) with
random initial strategy, a more informed initialization often yields a winning
strategy directly without any computation. (ii) This initialization makes SI
also yield smaller solutions. (iii) While Q-learning on the game graph turns
out not too efficient, Q-learning with the semantic information becomes
competitive to SI. Since already the simplest heuristics achieve significant
improvements the experimental results demonstrate the utility of semantic
labelling. This extra information opens the door to more advanced learning
approaches both for initialization and improvement of strategies
Looking at Mean-Payoff through Foggy Windows
Mean-payoff games (MPGs) are infinite duration two-player zero-sum games
played on weighted graphs. Under the hypothesis of perfect information, they
admit memoryless optimal strategies for both players and can be solved in
NP-intersect-coNP. MPGs are suitable quantitative models for open reactive
systems. However, in this context the assumption of perfect information is not
always realistic. For the partial-observation case, the problem that asks if
the first player has an observation-based winning strategy that enforces a
given threshold on the mean-payoff, is undecidable. In this paper, we study the
window mean-payoff objectives that were introduced recently as an alternative
to the classical mean-payoff objectives. We show that, in sharp contrast to the
classical mean-payoff objectives, some of the window mean-payoff objectives are
decidable in games with partial-observation
Determinising Parity Automata
Parity word automata and their determinisation play an important role in
automata and game theory. We discuss a determinisation procedure for
nondeterministic parity automata through deterministic Rabin to deterministic
parity automata. We prove that the intermediate determinisation to Rabin
automata is optimal. We show that the resulting determinisation to parity
automata is optimal up to a small constant. Moreover, the lower bound refers to
the more liberal Streett acceptance. We thus show that determinisation to
Streett would not lead to better bounds than determinisation to parity. As a
side-result, this optimality extends to the determinisation of B\"uchi
automata