IMPACTing SHOP: Foundations for integrating HTN Planning and Multi-Agency

In this paper we describe a formalism for integrating the SHOP HTN planning system with the IMPACT multi-agent environment. Our formalism provides an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT's capabilities for interacting with external agents, performing mixed symbolic/numeric computations, and making queries to distributed, heterogeneous information sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that this agentized version of SHOP will preserve soundness and completeness if certain conditions are met. (This technical report is the updated version of CS-TR-4085) (Also cross-referenced as UMIACS-TR-2000-02

HTN Planning in Answer Set Programming

In this paper we introduce a formalism for solving Hierarchical Task Network (HTN) Planning using Answer Set Programming (ASP). The ASP paradigm evolved out of the stable semantics for logic programs in recent years and is strongly related to nonmonotonic logics. We consider the formulation of HTN planning as described in the SHOP planning system and dene a systematic translation method from SHOP's representation of the planning problem into logic programs with negation. We show that our translation is sound and complete: answer sets of the logic program obtained by our translation correspond exactly to the solutions of the planning problem. Our approach does not rely on a particular system for computing answer sets. It can therefore serve as a means to evaluate ASP systems by using well-established benchmarks from the planning community. We tested our method on various such benchmarks and used smodels and DLV for computing answer sets. We compared our method to (1) similar approaches based on non-HTN planning and (2) SHOP, a dedicated planning system. We show that our approach outperforms non-HTN methods and that its performance is closer to that of SHOP, when we are using ASP systems which allow for nonground programs

Planning in Answer Set Programming using Ordered Task Decomposition

In this paper we introduce a formalism for solving Hierarchical Task Network (HTN) Planning using Answer Set Programming (ASP). We consider the formulation of HTN planning as described in the SHOP planning system and define a systematic translation method from SHOP's representation of the planning problem into logic programs with negation. We show that our translation is sound and complete: answer sets of the logic program obtained by our translation correspond exactly to the solutions of the planning problem. We compare our method to (1) similar approaches based on non-HTN planning and (2) SHOP, a dedicated planning system. We show that our approach outperforms non-HTN methods and that its performance is better with ASP systems that allow for nonground programs than with ASP systems that require ground programs. Keywords: HTN planning, nonmonotonic reasoning, ASP systems, benchmark

Planning in a Multi-Agent Environment: Theory and Practice

We give the theoretical foundations and empirical evaluation of a planning agent, shop, performing HTN planning in a multi-agent environment. shop is based on A-SHOP, an agentized version of the original SHOP HTN planning algorithm, and is integrated in the IMPACT multi-agent environment. We ran several experiments involving accessing various distributed, heterogeneous information sources, based on simplified versions of noncombatant evacuation operations, NEO's. As a result, we noticed..

IMPACTing SHOP: Putting an AI Planner into a Multi-Agent Environment

In this paper we describe a formalism for integrating the SHOP HTN planning sys-tem with the IMPACT multi-agent environment. We define the A-SHOP algorithm, an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT’s capabilities for interacting with external agents, performing mixed sym-bolic/numeric computations, and making queries to distributed, heterogeneous in-formation sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that A-SHOP is both sound and complete if certain conditions are met

Overexpression of glutathione S-transferase A1-1 in ECV 304 cells protects against busulfan mediated G2-arrest and induces tissue factor expression

1. The antineoplastic drug busulfan is frequently used in preconditioning regimens for bone marrow transplantation. Pharmacokinetics vary tremendously between patients due to extensive metabolism in the liver via conjugation to glutathione catalysed by glutathione S-transferase (GST) A1-1. Since elevated busulfan plasma levels have been reported to be a risk factor for developing veno-occlusive disease (VOD), metabolism of busulfan may play a pivotal role in the induction of VOD. 2. Therefore, we developed a cell model to investigate the influence of busulfan metabolism on its biological effects. GSTA1-1 cDNA was transfected into the cell line ECV 304 and protein expression was demonstrated by Western blotting. Enzymatic activity could be detected by formation of tetrahydrothiophene. Additionally, effects of busulfan treatment on cell cycle and expression of tissue factor have been investigated. 3. A busulfan-induced G2-arrest was reduced in GSTA1-1-transfected cells, which consequently displayed a significantly higher activity of cdc2 kinase (24.1±1.5 AU mg(−1) protein) after busulfan treatment compared to controls (14.7±2.3 AU mg(−1) protein; P<0.01). Elevated basal expression of tissue factor in GSTA1-1-transfected ECV 304 cells could be 4 fold increased by busulfan treatment. 4. These data demonstrate that ECV 304 cells transfected with GSTA1-1 provide a valuable tool to assess busulfan metabolism in vitro. Furthermore, overexpression of GSTA1-1 leads to a partial protection against cell cycle effects of busulfan and affects tissue factor expression