An Introduction to MultiAgent Systems.

Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. 

In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.

·Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges
·Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis
·Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics

Table of Contents

Preface; Introduction; Constraint Networks; Consistency-Enforcing Algorithms: Constraint Propagation; Directional Consistency; General Search Strategies; General Search Strategies: Look-Back; Local Search Algorithms; Advanced Consistency Methods; Tree-Decomposition Methods; Hybrid of Search and Inference: Time-Space Trade-offs; Tractable Constraint Languages; Temporal Constraint Networks; Constraint Optimization; Probabilistic Networks; Constraint Logic Programming; Bibliography

Constraint Processing

Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.

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Statistical Fraud Detection: A Review

It has recently been shown that local search is surprisingly good at finding satisfying assignments for certain computationally hard classes of CNF formulas. The performance of basic local search methods can be further enhanced by introducing mechanisms for escaping from local minima in the search space. We will compare three such mechanisms: simulated annealing, random noise, and a strategy called "mixed random walk". We show that mixed random walk is the superior strategy. We also present results demonstrating the effectiveness of local search with walk for solving circuit synthesis and circuit diagnosis problems. Finally, we demonstrate that mixed random walk improves upon the best known methods for solving MAX-SAT problems.

https://www.aaai.org/Papers/AAAI/1994/AAAI94-051.pdf

Noise strategies for improving local search

Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.

Deep Learning: A Critical Appraisal

The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

Many fraud analysis systems have at their heart a rule-based engine for generating alerts about suspicious behaviors. The rules in the system are usually based on expert knowledge. Automatic rule discovery aims at using past examples of fraudulent and legitimate usage to find new patterns and rules to help distinguish between the two. Some aspects of the problem of finding rules suitable for fraud analysis make this problem unique. Among them are the following: the need to find rules combining both the properties of the customer (e.g., credit rating) and properties of the specific “behavior” which indicates fraud (e.g., number of international calls in one day); and the need for a new definition of accuracy: We need to find rules which do not necessarily classify correctly each individual “usage sample” as either fraudulent or not, but ensure the identification, with a minimum of wasted cost and effort, of most of the fraud “cases” (i.e., defrauded customers). These aspects require a special-purpose rule discovery system. We present as an example a two-stage system based on adaptation of the C4.5 rule generator, with an additional rule selection mechanism. Our experimental results indicate that this route is very promising.

/pdf/discovery-of-fraud-rules-for-telecommunications-challenges-2ro2mdlvv7.pdf

Discovery of fraud rules for telecommunications—challenges and solutions

Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge

We define a model-theoretic reasoning formalism that is naturally implemented on symmetric neural networks (like Hopfield networks or Boltzman machines). We show that every symmetric neural network, can be seen as performing a search for a satisfying model of some knowledge that is wired into the network's weights. Several equivalent languages are then shown to describe the knowledge embedded in these networks. Among them is propositional calculus extended by augmenting propositional assumptions with penalties. The extended calculus is useful in expressing default knowledge, preference between arguments, and reliability of assumptions in an inconsistent knowledge base. Every symmetric network can be described by this language and any sentence in the language is translatable into such a network, A sound and complete proof procedure supplements the model-theoretic definition and gives an intuitive understanding of the nonmonotonic behavior of the reasoning mechanism. Finally, we sketch a connectionist inference engine that implements this reasoning paradigm.

/pdf/propositional-non-monotonic-reasoning-and-inconsistency-in-cmiv7x5uta.pdf

Propositional non-monotonic reasoning and inconsistency in symmetric neural networks

Connectionist networks with symmetric weights (like Hopfield networks and Boltzmann Machines) use gradient descent to find a minimum for quadratic energy functions. We show equivalence between the problem of satisfiability in propositional calculus and the problem of minimizing those energy functions. The equivalence is in the sense that for any satisfiable well-formed formula (WFF) we can find a quadratic function that describes it, such that the set of solutions that minimizes the function is equal to the set of truth assignments that satisfy the WFF. We also show that in the same sense every quadratic energy function describes some satisfiable WFF. Algorithms are given to transform any propositional WFF into an energy function that describes it and vice versa. High-order models that use sigma-pi units are shown to be equivalent to the standard quadratic models with additional hidden units. An algorithm to convert high-order networks to low-order ones is used to implement a satisfiability problem-solver on a connectionist network. The results give better understanding of the role of hidden units and of the limitations and capabilities of symmetric connectionist models. The techniques developed for the satisfiability problem may be applied to a wide range of other problems, such as associative memories, finding maximal consistent subsets, automatic deduction, and even nonmonotonic reasoning.

/pdf/symmetric-neural-networks-and-propositional-logic-cy0vxu88mq.pdf

Gadi Pinkas

Papers

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

Discovery of fraud rules for telecommunications—challenges and solutions

Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge

Propositional non-monotonic reasoning and inconsistency in symmetric neural networks

Symmetric Neural Networks and Propositional Logic Satisfiability