chandra reddy

Abstract Planners in the steel industry must design a set of steel slabs to satisfy the order book subject to constraints on (1) achieving a total designed weight for each order using multiples of an order-specific production size range, (2) minimum and maximum sizes for each ...

We describe an optimization tool for a multistage production process for rectangular steel plates. The problem we solve yields a production design (or plan) for rectangular plate products in a steel plant, i.e., a detailed list of operational steps and intermediate products on the way to producing steel plates. We decompose this problem into subproblems that correspond to the production stages, where one subproblem requires the design of casts by sequencing slabs which, in turn, have to be designed from mother plates. The design of mother plates consists of a two-dimensional packing problem. We develop a solution approach which combines mathematical programming models with search techniques from artificial intelligence. The use of these tools provides two types of benefits: improvements in the productivity of the plant and an approach to making the key business performance indicators, such as available-to-promise at a production level, operational.

In this paper, we consider learning first-order Horn programs from entailment. In particular, we show that any subclass of first-order acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomial-time subsumption procedure and satisfies some closure conditions. One consequence of this is that first-order acyclic determinate Horn programs with constant arity are exactly learnable from equivalence and entailment membership queries.

Exercises are problems ordered in increas-ing order of di culty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational rea-son for this is that the knowledge gained by solving simple problems is useful in e ciently solving more di cult ...

A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains.

Speedup learning is the study of improving the problem-solving performance with expe-rience and from outside guidance. We de-scribe here a system that successfully com-bines the best features of Explanation-based learning and empirical learning to learn goal ...

A Horn definition is a set of Horn clauses with the same predicate in all head literals. In this paper, we consider learning non-recursive, first-order Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Finally, we apply our results to learning control knowledge for efficient planning in the form of goal-decomposition rules.