An Introduction to MultiAgent Systems.

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Automated Planning: Theory and Practice.

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Logical Foundations of Probability By Rudolf Carnap Pp xvii + 607 (London: Routledge and Kegan Paul, Ltd, 1951) 42s net

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Logical Foundations of Probability

Logical models of arguement formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas that characterize different logical models of argument. It presents the formal features of a few features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentation from the mid-1980s, when argument systems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument in embedded in different complex systems for real-world applications, and allow more formal work to be done in different areas, such as AI and Law, case-based reasoning and negotiation among intelligent agents.

Logical models of argument

Conditionals in Nonmonotonic Reasoning and Belief Revision

Belief Revision and Nonmonotonic Reasoning - State of the Art.- Conditionals.- Revising Epistemic States by Conditional Beliefs.- Characterizing the Principle of Minimum Cross-Entropy.- Reasoning at Optimum Entropy.- Belief Revision and Nonmonotonic Reasoning - Revisited.- Knowledge Discovery by Following Conditional Structures.- Algorithms and Implementations.- Conclusion.

Conditionals in Nonmonotonic Reasoning and Belief Revision: Considering Conditionals as Agents

http://cs.uns.edu.ar/~grs/Papers/FalappaKernIsbernerSimari-AIJ2002.pdf

Explanations, belief revision and defeasible reasoning

Characterizing the principle of minimum cross-entropy within a conditional-logical framework

http://haendel.kr.tuwien.ac.at/research/reports/rr0212.pdf

Gabriele Kern-Isberner

Papers

Conditionals in Nonmonotonic Reasoning and Belief Revision

Conditionals in Nonmonotonic Reasoning and Belief Revision: Considering Conditionals as Agents

Explanations, belief revision and defeasible reasoning

Characterizing the principle of minimum cross-entropy within a conditional-logical framework

Combining probabilistic logic programming with the power of maximum entropy