Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Universal Cellular Automata and Class 4

Wolfram has provided a qualitative classification of cellular automata(CA) rules according to which, there exits a class of CA rules (called Class 4) which exhibit complex pattern formation and long-lived dynamical activity (long transients). These properties of Class 4 CA's has led to the conjecture that Class 4 rules are Universal Turing machines i.e. they are bases for computational universality. We describe an embedding of a ``small'' universal Turing machine due to Minsky, into a cellular automaton rule-table. This produces a collection of $(k=18,r=1)$ cellular automata, all of which are computationally universal. However, we observe that these rules are distributed amongst the various Wolfram classes. More precisely, we show that the identification of the Wolfram class depends crucially on the set of initial conditions used to simulate the given CA. This work, among others, indicates that a description of complex systems and information dynamics may need a new framework for non-equilibrium statistical mechanics.Comment: Latex, 10 pages, 5 figures uuencode

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component is the development of efficient search structures to sample possibility distributions based on the DPY transformation as introduced by Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model that allows us to express both possibilistic and probabilistic uncertainty, where the hybrid model is a proper extension of both probabilistic and possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can readily be applied to solve such hybrid models. © Springer International Publishing Switzerland 2016.This work is partially funded by EPSRC PACES project (Ref: EP/J012149/1).Peer Reviewe

A Comparison of Some Fuzzy Relation-based Linguistic Preference Models for Multiple-Factor Project Assessment

Some approaches to the use of linguistic-preference models based on fuzzy relations in the context of multiple factor project assessment are considered. Projects are characterized in terms of linguistic expressions of 'performance' with respect to factors or impacts and the 'importance' of those factors and impacts. Some variations of methods by Wilhelm and Parsaei (1991) and Eldukair and Ayyub (1992) are considered with some possible analogous methods. A simple illustrative, hypothetical example is developed to compare methods in the context of a proposed bridge river crossing in the city of Brisbane, Queensland, Australia, assessed against six factors: (1) cost, (2) lifespan, (3) usage, (4) aesthetics, (5) construction time, and (6) environmental impact

Medical concepts related to individual risk are better explained with "plausibility" rather than "probability"

BACKGROUND: The concept of risk has pervaded medical literature in the last decades and has become a familiar topic, and the concept of probability, linked to binary logic approach, is commonly applied in epidemiology and clinical medicine. The application of probability theory to groups of individuals is quite straightforward but can pose communication challenges at individual level. Few articles by the way have tried to focus the concept of "risk" at the individual subject level rather than at population level. DISCUSSION: The author has reviewed the conceptual framework which has led to the use of probability theory in the medical field in a time when the principal causes of death were represented by acute disease often of infective origin. In the present scenario, in which chronic degenerative disease dominate and there are smooth transitions between health and disease the use of fuzzy logic rather than binary logic would be more appropriate. The use of fuzzy logic in which more than two possible truth-value assignments are allowed overcomes the trap of probability theory when dealing with uncertain outcomes, thereby making the meaning of a certain prognostic statement easier to understand by the patient. SUMMARY: At individual subject level the recourse to the term plausibility, related to fuzzy logic, would help the physician to communicate to the patient more efficiently in comparison with the term probability, related to binary logic. This would represent an evident advantage for the transfer of medical evidences to individual subjects

Development of DFSI using Fuzzy Logic to Analyze Risk Levels of Driving Activity

The objective of this study is to develop a Driving Fatigue Strain Index using fuzzy logic to analyze the risk levels of driving activity among road users. Driving fatigue is always related to the driving activity and has been identified as one of the vital contributors to the road accidents and fatalities in Malaysia. Therefore, the present paper introduces the use of fuzzy logic for the development of strain index to provide the systematic analysis and propose an appropriate solution in minimizing the number of road accidents and fatalities. The development of strain index is based on the six risk factors associated with driving fatigue; muscle activity, heart rate, hand grip pressure force, seat pressure distribution, whole-body vibration, and driving duration. The data is collected for all the risk factors and consequently, the three conditions or risk levels are defined as “safe”, “slightly unsafe”, and “unsafe”. A membership function is defined for each fuzzy conditions. IF-THEN rules were used to define the input and output variables which correspond to physical measures. This index is a reliable advisory tool for providing analysis and solutions to driving fatigue problem, which constitutes the first effort toward the minimization of road accidents and fatalities

Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy