Recognition by directed attention to recursively partitioned images

A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth

Combined decision procedures for nonlinear arithmetics, real and complex

We describe contributions to algorithmic proof techniques for deciding the satisfiability of boolean combinations of many-variable nonlinear polynomial equations and inequalities over the real and complex numbers. In the first half, we present an abstract theory of Grobner basis construction algorithms for algebraically closed fields of characteristic zero and use it to introduce and prove the correctness of Grobner basis methods tailored to the needs of modern satisfiability modulo theories (SMT) solvers. In the process, we use the technique of proof orders to derive a generalisation of S-polynomial superfluousness in terms of transfinite induction along an ordinal parameterised by a monomial order. We use this generalisation to prove the abstract (“strategy-independent”) admissibility of a number of superfluous S-polynomial criteria important for efficient basis construction. Finally, we consider local notions of proof minimality for weak Nullstellensatz proofs and give ideal-theoretic methods for computing complex “unsatisfiable cores” which contribute to efficient SMT solving in the context of nonlinear complex arithmetic. In the second half, we consider the problem of effectively combining a heterogeneous collection of decision techniques for fragments of the existential theory of real closed fields. We propose and investigate a number of novel combined decision methods and implement them in our proof tool RAHD (Real Algebra in High Dimensions). We build a hierarchy of increasingly powerful combined decision methods, culminating in a generalisation of partial cylindrical algebraic decomposition (CAD) which we call Abstract Partial CAD. This generalisation incorporates the use of arbitrary sound but possibly incomplete proof procedures for the existential theory of real closed fields as first-class functional parameters for “short-circuiting” expensive computations during the lifting phase of CAD. Identifying these proof procedure parameters formally with RAHD proof strategies, we implement the method in RAHD for the case of full-dimensional cell decompositions and investigate its efficacy with respect to the Brown-McCallum projection operator. We end with some wishes for the future

The local geometry of multiattribute tradeoff preferences

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 125-129).Existing preference reasoning systems have been successful in simple domains. Broader success requires more natural and more expressive preference representations. This thesis develops a representation of logical preferences that combines numerical tradeoff ratios between partial outcome descriptions with qualitative preference information. We argue our system is unique among preference reasoning systems; previous work has focused on qualitative or quantitative preferences, tradeoffs, exceptions and generalizations, or utility independence, but none have combined all of these expressions under a unified methodology. We present new techniques for representing and giving meaning to quantitative tradeoff statements between different outcomes. The tradeoffs we consider can be multi-attribute tradeoffs relating more than one attribute at a time, they can refer to discrete or continuous domains, be conditional or unconditional, and quantified or qualitative. We present related methods of representing judgments of attribute importance. We then build upon a methodology for representing arbitrary qualitative ceteris paribus preference, or preferences "other things being equal," as presented in [MD04].(cont.) Tradeoff preferences in our representation are interpreted as constraints on the partial derivatives of the utility function. For example, a decision maker could state that "Color is five times as important as price, availability, and time," a sentiment one might express in the context of repainting a home, and this is interpreted as indicating that utility increases in the positive color direction five times faster than utility increases in the positive price direction. We show that these representations generalize both the economic notion of marginal rates of substitution and previous representations of preferences in AI.by Michael McGeachie.Ph.D

Local Geometry of Multiattribute Tradeoff Preferences

PhD thesisExisting preference reasoning systems have been successful insimple domains. Broader success requires more natural and moreexpressive preference representations. This thesis develops arepresentation of logical preferences that combines numericaltradeoff ratios between partial outcome descriptions withqualitative preference information. We argue our system is uniqueamong preference reasoning systems; previous work has focused onqualitative or quantitative preferences, tradeoffs, exceptions andgeneralizations, or utility independence, but none have combinedall of these expressions under a unified methodology.We present new techniques for representing and giving meaning toquantitative tradeoff statements between different outcomes. Thetradeoffs we consider can be multi-attribute tradeoffs relatingmore than one attribute at a time, they can refer to discrete orcontinuous domains, be conditional or unconditional, andquantified or qualitative. We present related methods ofrepresenting judgments of attribute importance. We then buildupon a methodology for representing arbitrary qualitative ceteris paribuspreference, or preferences ``other things being equal," aspresented in MD04. Tradeoff preferences inour representation are interpreted as constraints on the partialderivatives of the utility function. For example, a decision makercould state that ``Color is five times as important as price,availability, and time," a sentiment one might express in thecontext of repainting a home, and this is interpreted asindicating that utility increases in the positive color directionfive times faster than utility increases in the positive pricedirection. We show that these representations generalize both theeconomic notion of marginal rates of substitution and previousrepresentations of preferences in AI

Canonical queries as a query answering device (Information Science)

Issued as Annual reports [nos. 1-2], and Final report, Project no. G-36-60

Relational knowledge and representation for reinforcement learning

In reinforcement learning, an agent interacts with the environment, learns from feedback about the quality of its actions, and improves its behaviour or policy in order to maximise its expected utility. Learning efficiently in large scale problems is a major challenge. State aggregation is possible in problems with a first-order structure, allowing the agent to learn in an abstraction of the original problem which is of considerably smaller scale. One approach is to learn the Q-values of actions which are approximated by a relational function approximator. This is the basis for relational reinforcement learning (RRL). We abstract the state with first-order features which consist of only variables, thereby aggregating similar states from all problems of the same domain to abstract states. We study the limitations of RRL due to this abstraction and introduce the concepts of consistent abstraction, subsumption of problems, and abstract-equivalent problems. We propose three methods to overcome the limitations, extending the types of problems our RRL method can solve. Next, to further improve the learning efficiency, we propose to learn different types of generalised knowledge. The policy is influenced by directed exploration based on multiple types of intrinsic rewards and avoids previously encountered dead ends. In addition, we incorporate model-based techniques to provide better quality estimates of the Q-values. Transfer learning is possible by directly leveraging the generalised knowledge to accelerate learning in a new problem. Lastly, we introduce a new class of problems which considers dynamic objects and time-bounded goals. We discuss the complications these bring to RRL and present some solutions. We also propose a framework for multi-agent coordination to achieve joint goals represented by time-bounded goals by decomposing a multi-agent problem into single-agent problems. We evaluate our work empirically in six domains to demonstrate its efficacy in solving large scale problems and transfer learning