Simple Temporal Networks (STNs) are used in many applications, as they provide a powerful and general tool for representing conjunctions of maximum delay constraints over ordered pairs of temporal variables. We introduce Hyper Temporal Networks (HyTNs), a strict generalization of STNs, to overcome the limitation of considering only conjunctions of constraints. In a Hyper Temporal Network a single temporal constraint may be defined as a set of two or more maximum delay constraints which is satisfied when at least one of these delay constraints is satisfied. As in STNs, a HyTN is consistent when a real value can be assigned to each temporal variable satisfying all the constraints. We show the computational complexity for this generalization and propose effective reduction algorithms for checking consistency of HyTNs unveiling the link with the field of Mean Payoff Games. HyTNs are meant as a light generalization of STNs offering an interesting compromise. On one side, as we show, there exist practical pseudo-polynomial time algorithms for checking consistency and computing feasible schedules for HyTNs. On the other side, HyTNs allow to express natural constraints that cannot be expressed by STNs like "trigger off an event exactly δ min after the occurrence of the last event in a set".

Abstract-Conditional Simple Temporal Network (CSTN) is a constraint-based graph-formalism for conditional temporal planning. It offers a more flexible formalism than the equivalent CSTP model of Tsamardinos, Vidal and Pollack, from which it was derived mainly as a sound formalization. Three notions of consistency arise for CSTNs and CSTPs: weak, strong, and dynamic. Dynamic consistency is the most interesting notion, but it is also the most challenging and it was conjectured to be hard to assess. Tsamardinos, Vidal and Pollack gave a doubly-exponential time algorithm for deciding whether a CSTN is dynamicallyconsistent and to produce, in the positive case, a dynamic execution strategy of exponential size. In the present work we offer a proof that deciding whether a CSTN is dynamicallyconsistent is coNP-hard and provide the first singly-exponential time algorithm for this problem, also producing a dynamic execution strategy whenever the input CSTN is dynamically-consistent. The algorithm is based on a novel connection with Mean Payoff Games, a family of two-player infinite games played on finite graphs, well known for having applications in model-checking and formal verification. The presentation of such connection is mediated by the Hyper Temporal Network model, a tractable generalization of Simple Temporal Networks whose consistency checking is equivalent to determining Mean Payoff Games. In order to analyze the algorithm we introduce a refined notion of dynamic-consistency, named -dynamic-consistency, and present a sharp lower bounding analysis on the critical value of the reaction timeε where the CSTN transits from being, to not being, dynamically-consistent. The proof technique introduced in this analysis ofε is applicable more generally when dealing with linear difference constraints which include strict inequalities.

International audienceIn this work we offer an O(|V|² |E| W) pseudo-polynomial time deterministic algorithm for solving the Value Problem and Optimal Strategy Synthesis in Mean Payoff Games. This improves by a factor log(|V| W) the best previously known pseudo-polynomial time upper bound due to Brim et al. The improvement hinges on a suitable characterization of values, and a description of optimal positional strategies, in terms of reweighted Energy Games and Small Energy-Progress Measures

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The first output-sensitive algorithm for the Maximal Clique Listing problem was given by Tsukiyama et al. in 1977. As any algorithm falling within the Reverse Search paradigm, it performs a DFS visit of a directed tree (the RS-tree) having the objects to be listed (i.e., maximal cliques) as its nodes. In a recursive implementation, the RS-tree corresponds to the recursion tree of the algorithm. The time delay is given by the cost of generating the next child of a node, and Tsukiyama showed it is O(mn). In 2004, Makino and Uno sharpened the time delay to O(n ω ) by generating all the children of a node in one single shot, which is performed by computing a square fast matrix multiplication. In this paper, we further improve the asymptotics for the exploration of the same RS-tree by grouping the offsprings' computation even further. Our idea is to rely on rectangular fast matrix multiplication in order to compute all the children of n 2 nodes in one single shot. According to the current upper bounds on square and rectangular fast matrix multiplication, with this the time delay improves from O(n 2.3728639 ) to O(n 2.093362 ).Proposition 4. Let C be a maximal clique of G = (V, E), where |V | = n and |E| = m.The index i(C) is computable within O(m + n) time.