Negin Nejati

Learning hierarchical task networks by observation

Knowledge-based planning methods offer benefits over classical techniques, but they are time consuming and costly to construct. There has been research on learning plan knowledge from search, but this can take substantial computer time and may even fail to find solutions on complex tasks. Here we describe another approach that observes sequences of operators taken from expert solutions to problems and learns hierarchical task networks from them. The method has similarities to previous algorithms for explanation-based learning, but differs in its ability to acquire hierarchical structures and in the generality of learned conditions. These increase the methods capability to transfer learned knowledge to other problems and supports the acquisition of recursive procedures. After presenting the learning algorithm, we report experiments that compare its abilities to other techniques on two planning domains. In closing, we review related work and directions for future research.

A goal- and dependency-directed algorithm for learning hierarchical task networks

This paper describes a system for learning domain-dependent knowledge in the form of goal-indexed Hierarchical Task Networks (HTNs). DLIGHT is a goal-directed incremental learning algorithm which observes solution traces and generates rules for solving problems. One of the main challenges in learning this kind of knowledge is determining a good level of generality. Analytical methods, such as explanation-based macro-operator learning, construct very specific structures that guarantee a successful execution when applicable but generalize poorly to new problems. Previous goal-directed learning approaches produce hierarchical rules with more relaxed preconditions, but the learned knowledge suffers from over-generality. Our approach builds on one such approach but it strikes a better balance between generality and specificity. This is done by carrying out a goal-dependency analysis to determine the structure of the hierarchy and precondition of each rule to follow the successful solutions more closely while maintaining generality. We hypothesize that this algorithm produces HTNs that generalize well and can solve problems efficiently. We evaluate the systems behavior experimentally in several planning scenarios and conclude with related work and future research paths.

Guiding inference through relational reinforcement learning

Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research.

Inductive generalization of analytically learned goal hierarchies

We describe a new approach for learning procedural knowledge represented as teleoreactive logic programs using relational behavior traces as input. This representation organizes task decomposition skills hierarchically and associate explicitly defined goals with them. Our approach integrates analytical learning with inductive generalization in order to learn these skills. The analytical component predicts the goal dependencies in a successful solution and generates a teleoreactive logic program that can solve similar problems by determining the structure of the skill hierarchy and skill applicability conditions (preconditions), which may be overgeneral. The inductive component experiments with these skills on new problems and uses the data collected in this process to refine the preconditions. Our system achieves this by converting the data collected during the problem solving experiments into the positive and negative examples of preconditions that can be learned with a standard Inductive Logic Programming system. We show that this conversion uses one of the main commitments of teleoreactive logic programs: associating all skills with explicitly defined goals. We claim that our approach uses less expert effort compared to a purely inductive approach and performs better compared to a purely analytical approach.