Agreement Computing

[EN] In this paper we introduce the concept of Agreement Computing, motivate the central role that the concept of agreement plays in open software systems and discuss a number of research challenges that need to be addressed to make the agreement computing vision a reality.Research supported by the Agreement Technologies CONSOLIDER project under contract CSD2007-0022 and INGENIO 2010 and by the Agreement Technologies COST Action, IC0801.Sierra Garcia, C.; Botti Navarro, VJ.; Ossowski, DS. (2011). Agreement Computing. KI - Künstliche Intelligenz. 25(1):57-61. https://doi.org/10.1007/s13218-010-0070-yS5761251Arcos JL, Esteva M, Noriega P, Rodríguez JA, Sierra C (2005) Engineering open environments with electronic institutions. Eng Appl Artif Intell 18(2):191–204Boella G, Noriega P, Pigozzi G, Verhagen H (2009) In: Dagstuhl seminar proceedings 09121: normative multi-agent systems.Henrik G, Wright V (1963) Norm and action, a logical enquiry. Routledge and Kegan Paul, LondonHermenegildo M, Albert E, López-García P, Puebla G (2005) Abstraction carrying code and resource-awareness. In: Principle and practice of declarative programming. ACM Press, New YorkJennings N, Faratin P, Lomuscio A, Parsons S, Sierra C, Wooldridge M (2001) Automated negotiation: prospects methods and challenges. Group Decis Negot 10(2):199–215Jøsang A, Ismail R, Boyd C (2007) A survey of trust and reputation systems for online service provision. Decis Support Syst 43(2):618–644Kalfoglou Y, Schorlemmer M (2003) IF-Map: an ontology-mapping method based on information-flow theory. In: Spaccapietra S, March S, Aberer K (eds) Journal on data semantics I. Lecture notes in computer science, vol 2800. Springer, Heidelberg, pp 98–127Ko RKL, Lee SSG, Lee EW (2009) Business process management (bpm) standards: a survey. Bus Process Manag J 15(5):744–791Kraus S (1997) Negotiation and cooperation in multi-agent environments. Artif Intell 94(1–2):79–97March J (1996) A preface to understanding how decisions happen in organizations. In: Organizational decision-making, Cambridge University Press, CambridgeNecula GC, Lee P (1996) Proof-carrying code. Tech repRoss A (1968) Directives and norms. Humanities, Atlantic HighlandsSierra C, Debenham J (2006) Trust and honour in information-based agency. In: Proceedings of the 5th international conference on autonomous agents and multiagent systems. ACM Press, New York, pp 1225–1232Simon HA Administrative behavior. Free Press (1997)Vasirani M, Ossowski S (2009) A market-inspired approach to reservation-based urban road traffic management. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems, IFAAMAS, pp. 617–62

Converses for Secret Key Agreement and Secure Computing

We consider information theoretic secret key agreement and secure function computation by multiple parties observing correlated data, with access to an interactive public communication channel. Our main result is an upper bound on the secret key length, which is derived using a reduction of binary hypothesis testing to multiparty secret key agreement. Building on this basic result, we derive new converses for multiparty secret key agreement. Furthermore, we derive converse results for the oblivious transfer problem and the bit commitment problem by relating them to secret key agreement. Finally, we derive a necessary condition for the feasibility of secure computation by trusted parties that seek to compute a function of their collective data, using an interactive public communication that by itself does not give away the value of the function. In many cases, we strengthen and improve upon previously known converse bounds. Our results are single-shot and use only the given joint distribution of the correlated observations. For the case when the correlated observations consist of independent and identically distributed (in time) sequences, we derive strong versions of previously known converses

Computing hybridization networks using agreement forests

Rooted phylogenetic trees are widely used in biology to represent the evolutionary history of certain species. Usually, such a tree is a simple binary tree only containing internal nodes of in-degree one and out-degree two representing specific speciation events. In applied phylogenetics, however, trees can contain nodes of out-degree larger than two because, often, in order to resolve some orderings of speciation events, there is only insufficient information available and the common way to model this uncertainty is to use nonbinary nodes (i.e., nodes of out-degree of at least three), also denoted as polytomies. Moreover, in addition to such speciation events, there exist certain biological events that cannot be modeled by a tree and, thus, require the more general concept of rooted phylogenetic networks or, more specifically, of hybridization networks. Examples for such reticulate events are horizontal gene transfer, hybridization, and recombination. Nevertheless, in order to construct hybridization networks, the less general concept of a phylogenetic tree can still be used as building block. More precisely, often, in a first step, phylogenetic trees for a set of species, each based on a distinctive orthologous gene, are constructed. In a second step, specific sets containing common subtrees of those trees, known as maximum acyclic agreement forests, are calculated, which are then glued together to a single hybridization network. In such a network, hybridization nodes (i.e., nodes of in-degree larger than or equal to two) can exist representing potential reticulate events of the underlying evolutionary history. As such events are considered as rare phenomena, from a biological point of view, especially those networks representing a minimum number of reticulate events, which is denoted as hybridization number, are of high interest. Consequently, in a mathematical aspect, the problem of calculating hybridization networks can be briefly described as follows. Given a set T of rooted phylogenetic trees sharing the same set of taxa, compute a hybridization network N displaying T with minimum hybridization number. In this context, we say that such a network N displays a phylogenetic tree T, if we can obtain T from N by removing as well as contracting some of its nodes and edges. Unfortunately, this is a computational hard problem (i.e., it is NP-hard), even for the simplest case given just two binary input trees. In this thesis, we present several methods tackling this NP-hard problem. Our first approach describes how to compute a representative set of minimum hybridization networks for two binary input trees. For that purpose, our approach implements the first non-naive algorithm - called allMAAFs - calculating all maximum acyclic agreement forests for two rooted binary phylogenetic trees on the same set of taxa. In a subsequent step, in order to maximize the efficiency of the algorithm allMAAFs, we have developed additionally several modifications each reducing the number of computational steps and, thus, significantly improving its practical runtime. Our second approach is an extension of our first approach making the underlying algorithm accessible to more than two binary input trees. For this purpose, our approach implements the algorithm allHNetworks being the first algorithm calculating all relevant hybridization networks displaying a set of rooted binary phylogenetic trees on the same set of taxa, which is a preferable feature when studying hybridization events. Lastly, we have developed a generalization of our second approach that can now deal with multiple nonbinary input trees. For that purpose, our approach implements the first non-naive algorithm - called allMulMAAFs - calculating a relevant set of nonbinary maximum acyclic agreement forests for two rooted (nonbinary) phylogenetic trees on the same set of taxa. Each of the algorithms above is integrated into our user friendly Java-based software package Hybroscale, which is freely available and platform independent, so that it runs on all major operating systems. Our program provides a graphical user interface for visualizing trees and networks. Moreover, it facilitates the interpretation of computed hybridization networks by adding specific features to its graphical representation and, thus, supports biologists in investigating reticulate evolution. In addition, we have implemented a method using a user friendly SQL-style modeling language for filtering the usually large amount of reported networks