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Using ilp to improve planning in hierarchical reinforcement learning
 The Tenth International Conference ILP2000
, 2000
"... Abstract. Hierarchical reinforcement learning has been proposed as a solution to the problem of scaling up reinforcement learning. The RLTOPs Hierarchical Reinforcement Learning System is an implementation of this proposal which structures an agent’s sensors and actions into various levels of repre ..."
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Abstract. Hierarchical reinforcement learning has been proposed as a solution to the problem of scaling up reinforcement learning. The RLTOPs Hierarchical Reinforcement Learning System is an implementation of this proposal which structures an agent’s sensors and actions into various levels of representation and control. Disparity between levels of representation means actions can be misused by the planning algorithm in the system. This paper reports on how ILP was used to bridge these representation gaps and shows empirically how this improved the system’s performance. Also discussed are some of the problems encountered when using an ILP system in what is inherently a noisy and incremental domain. 1
Hierarchical Reinforcement Learning: A Hybrid Approach
, 2002
"... In this thesis we investigate the relationships between the symbolic and subsymbolic methods used for controlling agents by artificial intelligence, focusing in particular on methods that learn. In light of the strengths and weaknesses of each approach, we propose a hybridisation of symbolic and su ..."
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In this thesis we investigate the relationships between the symbolic and subsymbolic methods used for controlling agents by artificial intelligence, focusing in particular on methods that learn. In light of the strengths and weaknesses of each approach, we propose a hybridisation of symbolic and subsymbolic methods to capitalise on the best features of each. We implement such a hybrid system, called Rachel which incorporates techniques from TeleoReactive Planning, Hierarchical Reinforcement Learning and Inductive Logic Programming. Rachel uses a novel representation of behaviours, ReinforcementLearnt Teleooperators (RLTops), which defines the behaviour in terms of its desired consequences but leaves the implementation of the policy to be learnt by reinforcement learning. An RLTop is an abstract, symbolic description of the purpose of a behaviour, and is used by Rachel both as a planning operator and as the definition of a reward function by which the behaviour can be learnt. Two new
Algorithms for approximate minimization of the difference between submodular functions, with applications. Extended version
, 2012
"... We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step. We empirically and theoretically show that the pe ..."
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We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step. We empirically and theoretically show that the periteration cost of our algorithms is much less than [30], and our algorithms can be used to efficiently minimize a difference between submodular functions under various combinatorial constraints, a problem not previously addressed. We provide computational bounds and a hardness result on the multiplicative inapproximability of minimizing the difference between submodular functions. We show, however, that it is possible to give worstcase additive bounds by providing a polynomial time computable lowerbound on the minima. Finally we show how a number of machine learning problems can be modeled as minimizing the difference between submodular functions. We experimentally show the validity of our algorithms by testing them on the problem of feature selection with submodular cost features. 1
Concept Reliability in Machine Learning
 Proceedings of the Second Midwest Artificial Intelligence and Cognitive Science Society Conference. J. Dinsmore and T. Koschmann (Eds
, 1990
"... Introduction Much machine learning research addresses inductive learning  learning relationships from a set of examples (Michalski (1986) provides an excellent introduction). For instance, some programs have been used to learn medical diagnostic rules from a database of patients whose diagnoses a ..."
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Introduction Much machine learning research addresses inductive learning  learning relationships from a set of examples (Michalski (1986) provides an excellent introduction). For instance, some programs have been used to learn medical diagnostic rules from a database of patients whose diagnoses are known. These programs examine a number of attributes (e.g. age, temperature, and pulse rate) for a set of examples whose classification (e.g. diagnosis) is known. This set of examples is termed a training set. Attributes tests are combined into logical rules which are used to predict the classification (e.g. if (age > 5) and (temperature > 100), then (preliminarydiagnosis = notnormal)). These rules are generally termed concepts. Reliability and induction The probability that a given concept will accurately classify a training set by chance alone, denoted here as P, is a fundamental cha
An Attribute Weight Setting Method for kNN Based Binary Classification using Quadratic Programming
 In Proceedings of 15th European Conference on Artificial Intelligence (ECAI
, 2002
"... Abstract. In this paper, we propose a new attribute weight setting method for kNN based classifiers using quadratic programming, which is particular suitable for binary classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploit ..."
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Abstract. In this paper, we propose a new attribute weight setting method for kNN based classifiers using quadratic programming, which is particular suitable for binary classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. Experiments show that our method is quite practical for various problems and can achieve a competitive performance. Another merit of the method is that it can use small training sets. 1.
Order Effects in Incremental Learning
"... this paper. We maintain that any viable theory of human learning must be based on this definition, and we will see that many common learning methods satisfy it, though they are seldom presented in these terms. We can loosen our definition somewhat to allow storage of a few competing knowledge struct ..."
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this paper. We maintain that any viable theory of human learning must be based on this definition, and we will see that many common learning methods satisfy it, though they are seldom presented in these terms. We can loosen our definition somewhat to allow storage of a few competing knowledge structures, or to allow a current structure with a number of possible successors, from which one is then selected. These variations still restrict memory to a manageable size. 2.2 Definitions of Order Effects
CLIP4: Hybrid inductive machine learning algorithm that generates inequality rules
, 2004
"... The paper describes a hybrid inductive machine learning algorithm called CLIP4. The algorithm first partitions data into subsets using a tree structure and then generates production rules only from subsets stored at the leaf nodes. The unique feature of the algorithm is generation of rules that invo ..."
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The paper describes a hybrid inductive machine learning algorithm called CLIP4. The algorithm first partitions data into subsets using a tree structure and then generates production rules only from subsets stored at the leaf nodes. The unique feature of the algorithm is generation of rules that involve inequalities. The algorithm works with the data that have large number of examples and attributes, can cope with noisy data, and can use numerical, nominal, continuous, and missingvalue attributes. The algorithm's flexibility and e#ciency are shown on several wellknown benchmarking data sets, and the results are compared with other machine learning algorithms. The benchmarking results in each instance show the CLIP4's accuracy, CPU time, and rule complexity. CLIP4 has builtin features like tree pruning, methods for partitioning the data (for data with large number of examples and attributes, and for data containing noise), dataindependent mechanism for dealing with missing values, genetic operators to improve accuracy on small data, and the discretization schemes. CLIP4 generates model of data that consists of wellgeneralized rules, and ranks attributes and selectors that can be used for feature selection.
Setting Attribute Weights for kNN Based Binary Classification via Quadratic Programming
"... Abstract. The kNearest Neighbour (kNN) method is a typical lazy learning paradigm for solving classification problems. Although this method was originally proposed as a nonparameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, w ..."
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Abstract. The kNearest Neighbour (kNN) method is a typical lazy learning paradigm for solving classification problems. Although this method was originally proposed as a nonparameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for kNN based classifiers using quadratic programming, which is particularly suitable for binary classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. To evaluate our method, we carried out a series of experiments on six established data sets. Experiments show that our method is quite practical for various problems and can achieve a stable increase in accuracy over the standard kNN method as well as a competitive performance. Another merit of the method is that it can use small training sets.
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
, 1991
"... j_ REPORT DOCUMENTATION PAGE OMBNo.07o4o188 Oijt31!C _e_3rt,r1 _ burden +c,r this, oile(tlOtl]f,nformatlOn,s estimated to average 1 hour per resooqse irlcIu_4_g rife t_me tor re_e'hmg irlstruGIOr;$, sear(rang e,rsDng dat _ sour¢_., _ather_r _:_r_dna!_t_l_r_g the _a[a needed. 3nd Como/etlng 3nO revi ..."
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j_ REPORT DOCUMENTATION PAGE OMBNo.07o4o188 Oijt31!C _e_3rt,r1 _ burden +c,r this, oile(tlOtl]f,nformatlOn,s estimated to average 1 hour per resooqse irlcIu_4_g rife t_me tor re_e'hmg irlstruGIOr;$, sear(rang e,rsDng dat _ sour¢_., _ather_r _:_r_dna!_t_l_r_g the _a[a needed. 3nd Como/etlng 3nO reviewing lhe c3t{_,<_l©rl,)f rr f,_rma_l©n S_'nd:ommertt _ re_arctmg this burden estbma_e or _rly other asDe _ of l_s c,_lecliQt _ 3f mtormat_O r_. mc)udlrlg sugg_tl_n _, f_r r@d_ng thl' _ DurGen to,a/a_hl_gT,,Dr _ _4eaclGuarter _ Ser,_ces, Curectorate for mforma_lorl ODerat_on' _ and ReoK)r%, 12'15 Je_fE, r_on
Joint Concept Formation
"... this paper, we present a joint concept formation system, SGNN, that extends the previous work of concept formation [Fisher, 1987; McKusick and Langley, 1991]. SGNN is able to generate either disjoint concept trees or acyclic directed concept graphs, according to the characteristics inherited in doma ..."
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this paper, we present a joint concept formation system, SGNN, that extends the previous work of concept formation [Fisher, 1987; McKusick and Langley, 1991]. SGNN is able to generate either disjoint concept trees or acyclic directed concept graphs, according to the characteristics inherited in domain data. Furthermore, with certain controls applied to the number of winners at each concept layer, SGNN can also be used to only construct disjoint concept trees regardless of the regularity inherited in data. It is demonstrated 15