Preface

• Biological networks on (a lattice of) Hybrid Automata, by Alberto Policriti (Università di Udine, Italy)
• Automata-theoretic hierarchies, by Damian Niwinski (Warsaw University, Poland)
• Multi-dimensional quantitative games: complexity and strategy synthesis, by Jean-Francois Raskin (Université Libre de Bruxelles, Belgium)
• Constructive decision theory: Decision theory with subjective states and outcomes, by Joseph Halpern (Cornell University, USA)

We would like to acknowledge the people, institutions, and companies which contributed to the success of this edition of GandALF.

Let us first thank the Advisory Board and the Steering Committee for giving us the opportunity and the honor to supervise GandALF 2012. Many thanks go to all the Program Committee members and the additional reviewers for their excellent work, the fruitful discussions and the active participation during the reviewing process. companies and all the members of the Organizing Committee for the great work.

Moreover, we would like to acknowledge the EasyChair organization for supporting all the tasks related to the selection of contributions, and EPTCS for hosting the proceedings. Finally, we gratefully acknowledge the financial support to GandALF 2012 from private and public sponsors, including the Università degli Studi di Napoli "Federico II", the Gruppo Nazionale per il Calcolo Scientifico, the Comune di Napoli, and the company Gruppo Tufano.

September 2012,
Marco Faella and Aniello Murano

Program Chairs

• Marco Faella (Università di Napoli "Federico II", Italy)
• Aniello Murano (Università di Napoli "Federico II", Italy)

Program Committee

• Veronique Bruyere (Université de Mons, Belgium)
• Agostino Cortesi (Università di Venezia, Italy)
• Luca de Alfaro (University of California, USA)
• Kousha Etessami (University of Edinburgh, Scotland, UK)
• Erich Grädel (RWTH Aachen University, Germany)
• Arie Gurfinkel (Carnegie Mellon, USA)
• Orna Kupferman (Hebrew Unversity, Israel)
• Martin Lange (University of Kassel, Germany)
• Carsten Lutz (University of Bremen, Germany)
• Oded Maler (Verimag, France)
• Nicolas Markey (CNRS Cachan, France)
• Anca Muscholl (Université Bordeaux 1, France)
• Margherita Napoli (Università di Salerno, Italy)
• Damian Niwinski (Universitet Warszawski, Poland)
• Carla Piazza (Università di Udine, Italy)
• Nir Piterman (University of Leicester, UK)
• Gabriele Puppis (University of Oxford, UK)
• Ramaswamy Ramanujam (IMSC Chennai, India)
• Natasha Sharygina (University of Lugano, Switzerland)
• Sven Schewe (University of Liverpool, UK)
• Mariëlle Stoelinga (University of Twente, The Netherlands)
• Enrico Tronci (Università di Roma "La Sapienza", Italy)
• Helmut Veith (Technische Universität Wien, Austria)
• Tiziano Villa (Università di Verona, Italy)
• Mahesh Viswanathan (University of Illinois, USA)

Steering Committee

• Mikolaj Bojańczyk (Warsaw University, Poland)
• Javier Esparza (University of Munich, Germany)
• Angelo Montanari (Università di Udine, Italy)
• Margherita Napoli (Università di Salerno, Italy)
• Mimmo Parente (Università di Salerno, Italy)
• Wolfgang Thomas (RWTH Aachen University, Germany)
• Wieslaw Zielonka (University of Paris7, France)

Advisory Board

• Stefano Crespi Reghizzi (Politecnico of Milan, Italy)
• Jozef Gruska (Masaryk University, Czech Republik)
• Oscar H. Ibarra (UCSB, USA)
• Andrea Maggiolo-Schettini (Università di Pisa, Italy)

Organizing Committee

• Valerio Maggio (Università di Napoli "Federico II", Italy)
• Marilena Marotta
• Dario Della Monica (Reykjavik University, Iceland)
• Stefano Minopoli (Università di Napoli "Federico II", Italy)
• Milena Pugliese

Additional Referees

Biological Networks on (a Lattice of) Hybrid Automata

We discuss the problem of designing and implementing formalisms suitable to represent continuous and discrete evolving processes, operating in a combined and cooperating fashion and capable to encompass some level of stochastic behavior. In this context we present a language allowing to write programs used, for example, to simulate Biological Networks and incorporating the possibility to model stochastic interactions. The language we employ is stochastic Concurrent Constraint Programming (sCCP), a stochastic process algebra admitting a semantics given in terms of stochastic Hybrid Automata with piecewise deterministic continuous dynamics. We then proceed outlining a framework in which it is possible to associate to each sCCP program, a collection of different hybrid models. The stochastic Hybrid Automata associated to a given sCCP program can be presented in a lattice, in which the position is determined by the degrees of discreteness and stochasticity to be found in the automaton. We conclude discussing some interesting properties of the automata in this lattice and observing, for example, how different discretization strategies naturally correspond to different implementation schemata for the same program.

Automata-theoretic hierarchies

Hierarchies appear in many places in computer science, as a tool to classify objects according to their complexity. For automata on infinite words and trees, a relevant complexity measure is the Rabin-Mostowski index of the acceptance condition. The strictness of the induced index hierarchy for alternating automata on infinite trees has been a major open problem eventually solved by Bradfield in 1998. We show a somewhat simpler argument here, based on an analogy with deterministic automata on infinite words via suitable games. We also present the state-of-the-art regarding the separation and reduction properties, which in classical examples orientate the hierarchies (Π vs. Σ). We will outline the connections between the automata-theoretic hierarchies and other hierarchies in the literature, in particular in the μ-calculus and descriptive set theory, and point to some intriguing open problems concerning decidability issues.

Multi-dimension Quantitative Games: Complexity and Strategy Synthesis

In mean-payoff games, the objective of the protagonist is to ensure that the limit average of an infinite sequence of numeric weights is nonnegative. In energy games, the objective is to ensure that the running sum of weights is always nonnegative. Multi-mean-payoff and multi-energy games replace individual weights by tuples, and the limit average (resp. running sum) of each coordinate must be (resp. remain) nonnegative. These games have applications in the synthesis of resource-bounded processes with multiple resources. In this talk, I will summarize several recent results that we have obtained recently:

• We prove the finite-memory determinacy of multi-energy games and show the inter-reducibility of multi-mean-payoff and multi-energy games for finite-memory strategies.
• We improve the computational complexity for solving both classes of games with finite-memory strategies: while the previously best known upper bound was ExpSpace, and no lower bound was known, we give an optimal coNP-complete bound. For memoryless strategies, we show that the problem of deciding the existence of a winning strategy for the protagonist is NP-complete.
• We provide an optimal symbolic and incremental algorithm to synthesis strategies in multi-energy games.
• We present the first solution of multi-mean-payoff games with infinite-memory strategies. We show that multi-mean-payoff games with mean-payoff- sup objectives can be decided in NP ∩ coNP, whereas multi-mean-payoff games with mean-payoff-inf objectives are coNP-complete.

Constructive decision theory: Decision theory with subjective states and outcomes

The standard approach in decision theory (going back to Savage) is to place a preference order on acts, where an act is a function from states to outcomes. If the preference order satisfies appropriate postulates, then the decision maker can be viewed as acting as if he has a probability on states and a utility function on outcomes, and is maximizing expected utility. This framework implicitly assumes that the decision maker knows what the states and outcomes are. That isn't reasonable in a complex situation. For example, in trying to decide whether or not to attack Iraq, what are the states and what are the outcomes? We redo Savage viewing acts essentially as syntactic programs. We don't need to assume either states or outcomes. However, among other things, we can get representation theorems in the spirit of Savage's theorems; for Savage, the agent's probability and utility are subjective; for us, in addition to the probability and utility being subjective, so is the state space and the outcome space. I discuss the benefits, both conceptual and pragmatic, of this approach. As I show, among other things, it provides an elegant solution to framing problems.

No prior knowledge of Savage's work is assumed. Further details can be found in the following paper:
L. Blume, D. Easley, and J. Y. Halpern. Redoing the foundations of decision theory. In Principles of Knowledge Representation and Reasoning: Proc. Tenth International Conference (KR 2006), pages 14--24, 2006. A longer version, with the title "Constructive decision theory", is available online.