Inconsistent Requirements: an Argumentation View
Authors: Laurent Perrussel, Pierre-Jean Charrel
Venue: Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence (2001)

In this article, we present a logical framework for reasoning about inconsistent requirements in the context of multi-viewpoint requirements engineering process. In order to analyse the sources of inconsistencies and to reason with inconsistent requirements, we present an argumentation view of the requirements. Intuitively, argumentation is a tool for reasoning with inconsistent knowledge: requirements are defined in terms of arguments (a conclusion with its support); then, a class of acceptable arguments is built (arguments with no counterarguments). We propose to characterize different classes of requirements which are ordered: from weakly confident to strongly confident (i.e. consistent). In the paper, we present inference rules to build intra and inter-viewpoint reasoning. Inference rules are issued from the classes of requirements. We show how this work is useful for the requirements engineers to analyse inconsistent fragments of requirements.A Multi-Agent System to the Common Management of a Renewable Resource: Application to Water Sharing M. Le Bars a,b and J.M. Attonaty b a LAMSADE laboratory Université Paris-Dauphine 75775 Paris Cedex 16. France. bINRA Station d'économie rurale, BP 01 78850 Grignon France. {lebarsm@aol.com;attonaty@grigon.inra.fr} Abstract. Water sharing has become an important problem in France. A lot of negotiations are taking place at a local level between farmers, water suppliers, public services and environmentalists to allocate water resources between users. The problem is to share water with respect of different criteria like economic (global output) ethical (disparities between actors) environmental (water savings). Different approaches have been already taken using linear programming or game theory, but they are always based on the hypothesis that decision-makers are completely rational, take into account few players and are often monoperiodic. We suggest that an Agent-Based Modelling (ABM) built with a Multi-Agent approach could help negotiations between different players by showing the consequences of water allocation rules and taking in consideration the players 'respective attitudes and their ability to change their behaviour. In this paper we will first present the model structure with the different types of agents modelled, how the model runs over a number of years and the first results of simulations. Keywords. Distributed Artificial Intelligence; Multi-Agent Systems Title: A new hybrid method for solving constraint optimization problems in anytime contexts Authors: Samir Loudni and Patrice Boizumault Affiliation: Ecole Des Mines de Nantes Abstract: In this paper, we present a new hybrid method for solving constraint optimization problems in anytime contexts. We use the Valued Constraint Satisfaction Problem (VCSP) framework to model numerous discrete optimization problems. Our method (VNS/LDS+CP) combines a Variable Neighborhood Search (VNS) scheme with Limited Discrepancy Search (LDS) using Constraint Propagation (CP) to evaluate cost and legality of moves made by VNS. Our experimental results on real-word problem instances demonstrate that our method clearly outperforms both LNS/CP/GR (another hybrid method which also relies on the VCSP framework) and other standard local search methods as Simulated-Annealing. This confirm the benefit of the use, in a local search, of the LDS partial search with constraint propagation. keywords: Anytime Problems, Constraint-Satisfaction, Constraint-Optimization, Local Search Methods, Hybrid Methods. Using Software Agents to avoid Collisions among Multiple Robots Markus Jäger Corporate Technology, Information and Communications Siemens AG 81739 Munich, Germany markus.jaeger@mchp.siemens.de AI Algorithms Collaborative Software Agents Cooperating Robots This paper describes a method where collaborative software agents are used to coordinate the independently planned trajectories of multiple mobile robots to avoid collisions and deadlocks among them. Whenever the distance between two robots drops below a certain value, the agents exchange information about the planned trajectories of the robots and determine whether they are in danger of a collision. If a possible collision is detected, the agents monitor the robots movements and, if necessary, insert idle times between certain segments of the trajectories in order to avoid the collision. Deadlocks among two or more robots occur if a number of robots block each other in a way such that none of them is able to continue along its trajectory without causing a collision. These deadlocks are reliably detected by the agents. After a deadlock is detected, alternative trajectories for each of the involved robots are successively planned until the deadlock is resolved. The agents use a combination of three fully distributed algorithms to reliably solve the task. They do not use any global synchronization. ----------- Authors Affiliations: - Cooperating Software Agents - Cooperating Robots - Collision Avoidance among multiple Robots - Area Partitioning - Area Coverage The title : Successive Search Method for Valued Constraint Satisfaction and Optimization Problems. Authors Names : Mohamed TOUNSI and Philippe DAVID Email Adresses : mohamed.tounsi@emn.fr and philippe.david@emn.fr Affiliation : Computer Science Department, Ecole des Mines de Nantes , 4 Rue Alfred Kastler 44307 Nantes, FRANCE. Abstract : In this paper we introduce a new method based on Russian Doll Search (RDS) for solving optimization problems expressed as Valued Constraint Satisfaction Problems (VCSPs). The RDS method solves problems of size n (where n is the number of variables) by replacing one search by n successive searches on nested subproblems using the results of each search to produce a better lower bound. The main idea of our method is to introduce the variables through the successive searches not one by one but by sets of k variables. We present two variants of our method: the first one where the number k is fixed, noted kfRDS; the second one, kvRDS, where k can be variable. Finally, we show that our method improves RDS on daily management of an earth observation satellite. Keywords : Constraint Satisfaction, VCSP, Optimization Problems. B-Course: A Web Service for Bayesian Data Analysis Petri Myllymaki, Tomi Silander, Henry Tirri, Pekka Uronen Complex Systems Computation Group (CoSCo) P.O.Box 26, Department of Computer Science FIN-00014 University of Helsinki, Finland URL: http://www.cs.Helsinki.FI/research/cosco/ B-Course (http://b-course.cs.helsinki.fi) is a free web-based online data analysis tool, which allows the users to analyze their data for multivariate probabilistic dependencies. These dependencies are represented as Bayesian network models. In addition to this, B-Course also offers facilities for inferring certain type of causal dependencies from the data. The software uses a novel "tutorial style" user-friendly interface which intertwines the steps in the data analysis with support material that gives an informal introduction to the Bayesian approach adopted. Although the analysis methods, modeling assumptions and restrictions are totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling. B-Course can be used with most web-browsers (even Lynx), and the facilities include features such as automatic missing data handling and discretization, a flexible graphical interface for probabilistic inference on the constructed Bayesian network models (for Java enabled browsers), automatic pretty-printed layout for the networks, exportation of the models, and analysis of the importance of the derived dependencies. In this paper we discuss both the theoretical design principles underlying the B-Course tool, and the pragmatic methods adopted in the implementation of the software. Artificial Neural Networks in Hydrological Watershed Modeling: Surface Flow Contribution from the Ungaged Parts of a Catchment Richard Chibanga1, Jean Berlamont2 and Joos Vandewalle3 1PhD student in the Civil Eng. Dept. and 2Prof. Civil Eng. Dept., and Head of Hydraulics Laboratory, 3 Prof. Head of Electrical Engineering Dept - SISTA/COSIC, Katholieke Universiteit, Kasteelpark Arenberg 40, 3001 Heverlee (Leuven), Belgium Abstract Watershed modeling is often faced with the difficulty of determining the flow contribution from the ungaged sections of the catchment. Where the main concern is making accurate streamflow forecasts at specific watershed locations, it is cost-effective and efficient to implement a simple system theoretic model. In this paper Artificial Neural Networks (ANNs) are used as system theoretic models to model the ungaged flows. Using data from the Kafue River sub-catchment in Zambia and a simple reservoir routing model, an estimate of the flow contribution from the ungaged sections is derived. Inputs: rainfall, evaporation, previous-time-step flow are fed to a series of Feedforward-Backpropagation ANNs with target-output the current derived flow. Selected best- performing ANNs are compared with Autoregressive Moving Average models with exogenous inputs (ARMAX) and they give accurate and more robust forecasts over long term than the best performing ARMAXs thereby making ANNs a viable alternative in time-series forecasting. Keywords: semi conceptual-system theoretic; Artificial neural networks; subsystem; tributary-runoff; forecasting; mapping. Title: Generation of Propagation Rules for Intentionally Defined Constraints Authors: Slim Abdennadher Computer Science Department, University of Munich Oettingenstr. 67, 80538 Munich, Germany Slim.Abdennadher@informatik.uni-muenchen.de Christophe Rigotti Laboratoire d'Ingenierie des Systemes d'Information Batiment 501, INSA Lyon, 69621 Villeurbanne Cedex, France Christophe.Rigotti@insa-lyon.fr Abstract: A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In previous work, different methods for automatic generation of propagation rules for constraints defined over finite domains have been proposed. In this paper, we present a method for generating propagation rules for constraint predicates defined by means of a constraint logic program. Keywords: Constraint solving, Machine learning, Rule-based programming Title : "Data Flow Coherence Criteria in ILP Tools" Authors : Smaranda Muresan Department of Computer Science, Columbia University, New York, USA smara@cs.columbia.edu Tudor Muresan Department of Computer Science, Technical Univ. of Cluj-Napoca, Cluj-Napoca, Romania tmuresan@cs.utcluj.ro Rodica Potolea Department of Computer Science, Technical Univ. of Cluj-Napoca, Cluj-Napoca, Romania potolea@cs.utcluj.ro Keywords : Inductive Logic Programming, Automatic Program Generation, Data Flow Coherence Criteria, Pruning the Search Space Abstract: In this paper we present a new method that uses data-flow coherence criteria in definite logic program generation. We outline three main advantages of these criteria supported by our results: i) drastically pruning the search space (around 90%), ii) reducing the set of positive examples and reducing or even removing the need for the set of negative examples, and iii) allowing the induction of predicates that are difficult or even impossible to generate by other methods. Besides these criteria, the approach takes into consideration the program termination condition for recursive predicates. The paper outlines some theoretical issues and implementation aspects of our system for automatic logic program induction. Title: An Expert Recommendation System using Concept-based Relevance Discernment Authors: Takashi Yukawa NTT Corporation NTT Communication Science Laboratories 2-4 Hikaridai, Seika-cho, Kyoto, Japan yukawa@cslab.kecl.ntt.co.jp Kaname Kasahara NTT Corporation NTT Communication Science Laboratories 2-4 Hikaridai, Seika-cho, Kyoto, Japan kaname@cslab.kecl.ntt.co.jp Tsuneaki Kato The University of Tokyo Graduate School of Arts and Science Dept. of Language and Information Science 3-1-8 Komaba, Meguro-ku, Tokyo, Japan kato@boz.c.u-tokyo.ac.jp Toshiro Kita NTT Communications Corporation Solution Business Division Yamato Seimei Bldg. 1-1-7, Uchisaiwai-cho Chiyoda-ku, Tokyo, Japan toshiro.kita@ntt.com Keywords: information retrieval, recommendation system, vector space model, concept base, knowledge management Abstract: An expert recommendation system using concept-based relevance discernment is proposed. This system processes the description of a technical topic as input and then finds engineers who have a high level of expertise in that area. The technique employed is an extended vector space model that locates both technical topics and engineers in the same multi-dimensional space, and then calculates their relevance. This system can also retrieve engineers or documents that are related to a field matching a given engineer's technical interests. Such a system can be expected to play the role of a person's professional network, and be a valuable tool for knowledge management among several organizations. Paper Title: Combinatorial Optimization through Statistical Instance-Based Learning Keywords: constructive search, heuristics, optimization, instance-based learning Authors: Orestis Telelis, Panagiotis Stamatopoulos Department of Informatics and Telecommunications University of Athens 157 84 Athens, Greece {telelis,takis}@di.uoa.gr Abstract: Different successful heuristic approaches have been proposed for solving combinatorial optimization problems. Commonly, each of them is specialized to serve a different purpose or address specific difficulties. However, most combinatorial problems that model real world applications have a priori well known measurable properties. Embedded machine learning methods may aid towards the recognition and utilization of these properties for the achievement of satisfactory solutions. In this paper, we present a heuristic methodology which employs the instance-based machine learning paradigm. This methodology can be adequately configured for several types of optimization problems which are known to have certain properties. Experimental results are discussed concerning two well known problems, namely the knapsack problem and the set partitioning problem. These results show that the proposed approach is able to find significantly better solutions compared to intuitive search methods based on heuristics which are usually applied to the specific problems. TITLE: Interleaved Backtracking in Distributed Constraint Networks > AUTHOR: Youssef Hamadi > AFFILIATION: Hewlett Packards Labs Filton road, Stoke Gifford, > Bristol BS34 8QZ, United Kingdom > EMAIL: yh@hplb.hpl.hp.com > > > Abstract > The adaptation of software technology to distributed > environments is an important challenge today. In this > work we combine parallel and distributed search. By > this way we add the potential speed up of a parallel > exploration in the processing of distributed problems. > This paper extends DIBT, a distributed search proce > dure operating in distributed constraint networks [6]. > The extension is twofold. First the procedure is up > dated to face delayed information problems upcoming > in heterogeneous systems. Second, the search is ex > tended to simultaneously explore independent parts of > a distributed search tree. By this way we introduce > parallelism into distributed search, which brings to In > terleaved Distributed Intelligent BackTracking (IDIBT). > Our results show that 1) insoluble problems do not > greatly degrade performance over DIBT and 2) super > linear speed up can be achieved when the distribution > of solution is nonuniform. > > Keywords: Distributed Constraint Satisfaction, Distributed AI, > Collaborative Software Agents, Search