Author name cluster

Kangheng Wu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

KER Journal 2007 Journal Article

ARMS: an automatic knowledge engineering tool for learning action models for AI planning

KANGHENG WU
Qiang Yang
YUNFEI JIANG

Abstract We present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.

Details DOI

AIJ Journal 2007 Journal Article

Learning action models from plan examples using weighted MAX-SAT

Qiang Yang
KANGHENG WU
YUNFEI JIANG

AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically.

Details DOI

ICAPS Conference 2005 Conference Paper

Learning Actions Models from Plan Examples with Incomplete Knowledge

Qiang Yang 0001
Kangheng Wu
Yunfei Jiang

AI planning requires the definition of an action model using a language such as PDDL as input. However, building an action model from scratch is a difficult and time-consuming task even for experts. In this paper, we develop an algorithm called ARMS for automatically discovering action models from a set of successful plan examples. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of the example plans; that is, we assume that no intermediate states are given. This requirement is motivated by a variety of applications, including object tracking and plan monitoring where the knowledge about intermediate states is either minimal or unavailable to the observing agent. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, our ARMS algorithm gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a propositional satisfiability (SAT) problem and solves it using a SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically

Details