We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

/pdf/the-ff-planning-system-fast-plan-generation-through-3vgjtylx0c.pdf

The FF planning system: fast plan generation through heuristic search

C4.5: Programs for Machine Learning (書評)

Handbook of logic in computer science.

http://www.umbertostraccia.it/cs/download/papers/JWS08/JWS08.pdf

Managing uncertainty and vagueness in description logics for the Semantic Web

In this paper we present fuzzyDL, an expressive fuzzy description logic reasoner.We present its salient features, including some novel concept constructs and queries, and examples of use cases: matchmaking and fuzzy control.

/pdf/fuzzydl-an-expressive-fuzzy-description-logic-reasoner-1pcdkgw3yo.pdf

fuzzyDL: An expressive fuzzy description logic reasoner

In [1] we have shown how to construct a 3-layered recurrent neural network that computes the fixed point of the meaning function T_P of a given propositional logic program {\cal P}, which corresponds to the computation of the semantics of P. In this article we consider the first order case. We define a notion of approximation for interpretations and prove that there exists a 3-layered feed forward neural network that approximates the calculation of T_P for a given first order acyclic logic program P with an injective level mapping arbitrarily well. Extending the feed forward network by recurrent connections we obtain a recurrent neural network whose iteration approximates the fixed point of T_P. This result is proven by taking advantage of the fact that for acyclic logic programs the function T_P is a contraction mapping on a complete metric space defined by the interpretations of the program. Mapping this space to the metric space R with Euclidean distance, a real valued function f_P can be defined which corresponds to T_P and is continuous as well as a contraction. Consequently it can be approximated by an appropriately chosen class of feed forward neural networks.

Approximating the Semantics of Logic Programs by Recurrent Neural Networks

In this paper we present a fuzzy description logic , where primitive concepts are modified by means of hedges. is strictly more expressive than Fuzzydefined in [8]. We show that given a linearly ordered set of hedges primitive concepts can be modified to any desired degree by prefixing them with appropriate chains of hedges. Furthermore, we define a decision procedure for the unsatisfiability problem in , and discuss truth bounds, expressivity as well as complexity issues.

A Fuzzy Description Logic with Hedges as Concept Modifiers

In this paper we present the fuzzy description logic ALCFH introduced, where primitive concepts are modified by means of hedges taken from hedge algebras. ALCFH is strictly more expressive than Fuzzy-ALC defined in [11]. We show that given a linearly ordered set of hedges primitive concepts can be modified to any desired degree by prefixing them with appropriate chains of hedges. Furthermore, we define a decision procedure for the unsatisfiability problem in ALCFH , and discuss knowledge base expansion when using terminologies, truth bounds, expressivity as well as complexity issues. We extend [8] by allowing modifiers on non-primitive concepts and extending the satisfiability procedure to handle concept definitions.

The Fuzzy Description Logic ALCFH with Hedge Algebras as Concept Modifiers

We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid alternation of classification and learning of new data. The algorithm generalizes known non-incremental algorithms for top down induction of fuzzy decision trees, as well as known incremental algorithms for induction of decision trees in classical logic. The algorithm is shown to be terminating and to yield results equivalent to the non-incremental version.

/pdf/incremental-fuzzy-decision-trees-lul79k8fi8.pdf

Incremental Fuzzy Decision Trees

It is rigorously shown how planning problems encoded as a class of entailment problems in the fluent calculus can be mapped onto satisfiability problems for propositional formulas, which in turn can be mapped to the problem of finding models using binary decision diagrams (BDDs). The mapping is shown to be sound and complete. First experimental results of an implementation are presented and discussed.

Hans-Peter Störr

Papers

Approximating the Semantics of Logic Programs by Recurrent Neural Networks

A Fuzzy Description Logic with Hedges as Concept Modifiers

The Fuzzy Description Logic ALCFH with Hedge Algebras as Concept Modifiers

Incremental Fuzzy Decision Trees

Solving the Entailment Problem in the Fluent Calculus Using Binary Decision Diagrams