scholarly journals Automated Discovery of Loop Invariants for High-Assurance Programs Synthesized Using AI Planning Techniques

2008 ◽  
Author(s):  
Jicheng Fu ◽  
Farokh B. Bastani ◽  
I-Ling Yen
Keyword(s):  
Ai Planning ◽  
Planning Techniques ◽  
High Assurance ◽  
Automated Discovery

DYNAMICALLY ORGANIZING KDD PROCESSES

2001 ◽  
Vol 15 (03) ◽  
pp. 451-473 ◽  
Author(s):  
NING ZHONG ◽  
CHUNNIAN LIU ◽  
SETSUO OHSUGA
Keyword(s):  
Knowledge Discovery ◽  
Data Model ◽  
Difficult Problem ◽  
Ai Planning ◽  
Global Learning ◽  
Planning Techniques ◽  
Discovery System ◽  
Multiple Levels ◽  
Entity Relationship ◽  
Core Problem

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

Applying integer programming to AI planning

2000 ◽  
Vol 15 (1) ◽  
pp. 85-100 ◽  
Author(s):  
THOMAS VOSSEN ◽  
MICHAEL BALL ◽  
AMNON LOTEM ◽  
DANA NAU
Keyword(s):  
Integer Programming ◽  
Future Research ◽  
Ai Planning ◽  
Planning Techniques ◽  
Historical Difference ◽  
Planning Problems

Despite the historical difference in focus between AI planning techniques and Integer Programming (IP) techniques, recent research has shown that IP techniques show significant promise in their ability to solve AI planning problems. This paper provides approaches to encode AI planning problems as IP problems, describes some of the more significant issues that arise in using IP for AI planning, and discusses promising directions for future research.

A Review of knowledge-based planning techniques

1984 ◽  
Vol 1 (2) ◽  
pp. 4-17 ◽  
Author(s):  
Austin Tate
Keyword(s):  
Historical Development ◽  
Research Topic ◽  
Ai Planning ◽  
Important Research ◽  
Planning Systems ◽  
Planning Techniques ◽  
Knowledge Based ◽  
Research Themes ◽  
Knowledge Based Planning ◽  
Active Research

SummaryPlanning systems have been an active research topic within Artificial Intelligence for over two decades. There have been a number of techniques developed during that period which still form an essential part of many of today's planners. This paper introduces the techniques, attempts to classify some of the important research themes in AI planning and describes their historical development.

Dyadic Obligations over Complex Actions as Deontic Constraints in the Situation Calculus

2020 ◽  
Author(s):  
Jens Claßen ◽  
James Delgrande
Keyword(s):  
Artificial Intelligence ◽  
Deontic Logic ◽  
Action Theory ◽  
Prima Facie ◽  
Situation Calculus ◽  
Artificial Agents ◽  
Planning Techniques ◽  
Course Of Action ◽  
Reasoning About Action ◽  
The Given

With the advent of artificial agents in everyday life, it is important that these agents are guided by social norms and moral guidelines. Notions of obligation, permission, and the like have traditionally been studied in the field of Deontic Logic, where deontic assertions generally refer to what an agent should or should not do; that is they refer to actions. In Artificial Intelligence, the Situation Calculus is (arguably) the best known and most studied formalism for reasoning about action and change. In this paper, we integrate these two areas by incorporating deontic notions into Situation Calculus theories. We do this by considering deontic assertions as constraints, expressed as a set of conditionals, which apply to complex actions expressed as GOLOG programs. These constraints induce a ranking of "ideality" over possible future situations. This ranking in turn is used to guide an agent in its planning deliberation, towards a course of action that adheres best to the deontic constraints. We present a formalization that includes a wide class of (dyadic) deontic assertions, lets us distinguish prima facie from all-things-considered obligations, and particularly addresses contrary-to-duty scenarios. We furthermore present results on compiling the deontic constraints directly into the Situation Calculus action theory, so as to obtain an agent that respects the given norms, but works solely based on the standard reasoning and planning techniques.

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