Rules and Apriori Algorithm in Non-deterministic Information Systems

This paper presents a framework of rule generation in Non-deterministic Information Systems (NISs), which follows rough sets based rule generation in Deterministic Information Systems (DISs). Our previous work about NISs coped with certain rules, minimal certain rules and possible rules. These rules are characterized by the concept of consistency. This paper relates possible rules to rules by the criteria support and accuracy in NISs. On the basis of the information incompleteness in NISs, it is possible to define new criteria, i.e., minimum support, maximum support, minimum accuracy and maximum accuracy. Then, two strategies of rule generation are proposed based on these criteria. The first strategy is Lower Approximation strategy, which defines rule generation under the worst condition. The second strategy is Upper Approximation strategy, which defines rule generation under the best condition. To implement these strategies, we extend Apriori algorithm in DISs to Apriori algorithm in NISs. A prototype system is implemented, and this system is applied to some data sets with incomplete information

Incentives and Efficiency in Uncertain Collaborative Environments

We consider collaborative systems where users make contributions across multiple available projects and are rewarded for their contributions in individual projects according to a local sharing of the value produced. This serves as a model of online social computing systems such as online Q&A forums and of credit sharing in scientific co-authorship settings. We show that the maximum feasible produced value can be well approximated by simple local sharing rules where users are approximately rewarded in proportion to their marginal contributions and that this holds even under incomplete information about the player's abilities and effort constraints. For natural instances we show almost 95% optimality at equilibrium. When players incur a cost for their effort, we identify a threshold phenomenon: the efficiency is a constant fraction of the optimal when the cost is strictly convex and decreases with the number of players if the cost is linear

FUNCTIONAL DEPENDENCIES AND INCOMPLETE INFORMATION

Functional dependencies play an important role in relational database design. They are defined in the context of a single relation which at all times must contain tuples with non-null entries. In this paper we examine an extension of the functional dependency interpretation to handle null values, that is, entries in tuples that represent incomplete information in a relational database. A complete axiomatization of inference rules for extended functional dependencies is also presented. Only after having such results is it possible to talk about decompositions and normalization theory in a context of incomplete information. Finally, we show that there are several practical advantages in using nulls and a weaker notion of constraint satisfiability.Information Systems Working Papers Serie

Strategic voting with incomplete information

Classical results in social choice theory on the susceptibility of voting rules to strategic manipulation make the assumption that the manipulator has complete information regarding the preferences of the other voters. In reality, however, voters only have incomplete information, which limits their ability to manipulate. We explore how these limitations affect both the manipulability of voting rules and the dynamics of systems in which voters may repeatedly update their own vote in reaction to the moves made by others. We focus on the Plurality, Veto, κ-approval, Borda, Copeland, and Maximin voting rules, and consider several types of information that are natural in the context of these rules, namely information on the current front-runner, on the scores obtained by each alternative, and on the majority graph induced by the individual preferences

A Formal Account of the Open Provenance Model

On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs

Information Flow Control-by-Construction for an Object-Oriented Language Using Type Modifiers

In security-critical software applications, confidential information must be prevented from leaking to unauthorized sinks. Static analysis techniques are widespread to enforce a secure information flow by checking a program after construction. A drawback of these systems is that incomplete programs during construction cannot be checked properly. The user is not guided to a secure program by most systems. We introduce IFbCOO, an approach that guides users incrementally to a secure implementation by using refinement rules. In each refinement step, confidentiality or integrity (or both) is guaranteed alongside the functional correctness of the program, such that insecure programs are declined by construction. In this work, we formalize IFbCOO and prove soundness of the refinement rules. We implement IFbCOO in the tool CorC and conduct a feasibility study by successfully implementing case studies

Argument-based Applications to Knowledge Engineering

Argumentation is concerned with reasoning in the presence of imperfect information by constructing and weighing up arguments. It is an approach for inconsistency management in which conflict is explored rather than eradicated. This form of reasoning has proved applicable to many problems in knowledge engineering that involve uncertain, incomplete or inconsistent knowledge. This paper concentrates on different issues that can be tackled by automated argumentation systems and highlights important directions in argument-oriented research in knowledge engineering. 1 Introduction One of the assumptions underlying the use of classical methods for representation and reasoning is that the information available is complete, certain and consistent. But often this is not the case. In almost every domain, there will be beliefs that are not categorical; rules that are incomplete, with unknown or implicit conditions; and conclusions that are contradictory. Therefore, we need alternative know..

A neuro-fuzzy architecture for real-time applications

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach

Towards a comparison criteria for CDeLP

The development of systems with the ability to reason about change notion and actions has been of great importance for the artificial intelligence community. The definition and implementation of systems capable of managing defeasible, incomplete, unreliable, or uncertain information has been also an area of much interest. With a few exceptions research on these two ways of reasoning was independently pursued. Nevertheless, they are complementary and closely related, since many applications that deal with defeasible information also depends on the occurrence of events and time. DeLP is an argumentative system appropriate for commonsense reasoning. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent’s knowledge and for providing an argumentation based reasoning mechanism for that agent (see for example [6, 1]). It is interesting to extend this system adding mechanisms to manage events and time as CDeLP [7]. Here we analyze how to develop a comparison criteria for arguments built up from causal information and considers commonsense rules of inertia.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI