Phan Huy Tu

Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming

We extend the 0-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation is NP-complete. We then present an answer set programming based conditional planner, called ASCP, that is capable of generating both conformant plans and conditional plans in the presence of sensing actions, incomplete information about the initial state, and static causal laws. We prove the correctness of our implementation and argue that our planner is sound and complete with respect to the proposed approximation. Finally, we present experimental results comparing ASCP to other planners.

Incremental Information Extraction Using Relational Databases

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this paper, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. Our experiments show that in the event of deployment of a new module, our incremental extraction approach reduces the processing time by 89.64 percent as compared to a traditional pipeline approach. By applying our methods to a corpus of 17 million biomedical abstracts, our experiments show that the query performance is efficient for real-time applications. Our experiments also revealed that our approach achieves high quality extraction results.

Planning with Sensing Actions and Incomplete Information Using Logic Programming

We present a logic programming based conditional planner that is capable of generating both conditional plans and conformant plans in the presence of sensing actions and incomplete information. We prove the correctness of our implementation and show that our planner is complete with respect to the 0-approximation of sensing actions and the class of conditional plans considered in this paper. Finally, we present preliminary experimental results and discuss further enhancements to the program.

An approximation of action theories of AL and its application to conformant planning

In this paper we generalize the notion of approximation of action theories introduced in [13,26]. We introduce a logic programming based method for constructing approximation of action theories of and prove its soundness. We describe an approximation based conformant planner and compare its performance with other state-of-the-art conformant planners.

CPP: a constraint logic programming based planner with preferences

We describe the development of a constraint logic programming based system, called CPP, which is capable of generating most preferred plans with respect to a users preference and evaluate its performance.

Construction of an agent-based framework for evolutionary biology: a progress report

We report on the development of an agent-based system, called ΦLOG, for the specification and execution of phylogenetic inference applications. We detail the implementation of the main components of the system. In the process, we discuss how advanced techniques developed in different research areas such as domain-specific languages, planning, Web Services discovery and invocation, and Web Service compositions can be applied in the building of the ΦLOG system.

Efficient reasoning about action and change in the presence of incomplete information and its application in planning

Many domains that we wish to model and reason about are subject to change due to the execution of actions. Representing and reasoning about dynamic domains play an important role in AI because they serve as a fundamental basis for many applications, including planning, diagnosis, and modelling. Research in the field focuses on the development of formalisms for reasoning about action and change (RAC). Such a formalism normally consists of two components: a representation language and a reasoning mechanism. It has been well known in the field that two criteria for the success of a formalism are its expressiveness and efficiency. The former means that the representation language is rich enough to describe complicated domains; the latter implies the reasoning mechanism is computationally efficient, making it possible to be implemented on a machine. Besides, in daily life, we have to face the absence of complete information and thus any formalism should take this matter into account.

Reasoning about sensing actions in domains with multi-valued fluents

In this paper, we discuss the weakness of current action languages for sensing actions with respect to modeling domains with multi-valued fluents. To address this problem, we propose a language with sensing actions and multi-valued fluents, called AMK, provide a transition function based semantics for the language, and demonstrate its use through several examples from the literature. We then define the entailment relationship between action theories and queries in AMK, denoted by ⊧AMK, and discuss some properties about AMK.