scholarly journals GM Plan: a gate matrix layout algorithm based on artificial intelligence planning techniques

1990 ◽  
Vol 9 (8) ◽  
pp. 836-845 ◽  
Author(s):  
Y.H. Hu ◽  
S.-J. Chen
Keyword(s):  
Artificial Intelligence ◽  
Planning Techniques ◽  
Layout Algorithm ◽  
Artificial Intelligence Planning ◽  
Gate Matrix

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

scholarly journals Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

2012 ◽  
Vol 367 (1603) ◽  
pp. 2723-2732 ◽  
Author(s):  
Jackie Chappell ◽  
Nick Hawes
Keyword(s):  
Artificial Intelligence ◽  
Associative Learning ◽  
Animal Cognition ◽  
Learning Processes ◽  
Simulation Environment ◽  
Physical Cognition ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
Simple Set ◽  
Complex Forms

Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended.

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.

Comparing HTN Planning Method With Grammar-Based Approach in Generative Design

2000 ◽  
Author(s):  
Hai Shi ◽  
Linda C. Schmidt
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Planning Method ◽  
Future Research ◽  
Planning Problem ◽  
Generative Design ◽  
Artificial Intelligence Planning ◽  
Design Alternatives ◽  
Htn Planning ◽  
Initial Comparison

Abstract In mechanical conceptual design, the more design alternatives generated, the higher the benefit to designers. In this paper we explore the use of HTN planning, an artificial intelligence planning method, to perform generative conceptual design. The HTN planning method is “goal driven” while the grammar method is “feasibility driven”. We mapped a grammar-based generative method for conceptual design of Meccano carts into an HTN planning problem format. An initial comparison of the two methods is provided in this paper. Exploring the use of a planning method provides a benchmark for future research in generative design.

scholarly journals SEGMA: A Simulated Evolution Gate-Matrix Layout Algorithm

VLSI Design ◽  
1994 ◽  
Vol 2 (3) ◽  
pp. 241-257 ◽  
Author(s):  
Chi-Yu Mao ◽  
Yu Hen Hu
Keyword(s):  
Biological Evolution ◽  
Poor Quality ◽  
Simulated Evolution ◽  
Layout Problem ◽  
Placement Problem ◽  
Layout Algorithm ◽  
Logic Modules ◽  
Gate Matrix ◽  
The Given ◽  
Heuristic Criterion

In this paper, we present a Simulated Evolution Gate Matrix layout Algorithm (SEGMA) for synthesizing CMOS random logic modules. The gate-matrix layout problem is solved as a one-dimensional transistor gates placement problem. Given a placement of all the transistor gates, simulated evolution offers a systematic method to improve the quality of the layout that is measured by the number of tracks needed for the given netlist. This is accomplished by identifying a subset of gates whose relative placements are deemed “poor quality” according to a heuristic criterion. By rearranging the placement of these identified subsets of gates, it is hoped that a gate placement with better quality, meaning fewer tracks, may emerge. Since this method enables the current “generation” of gate placement to evolve into a more advanced one in a way similar to the biological evolution process, this method is called simulated evolution. To apply simulated evolution to solve the gate-matrix layout problem, we propose a novel heuristic criterion, called randomized quality factor, which facilitates the judicious selection of the subset of poor quality gates. Several carefully devised and tested strategies are also implemented. Extensive simulation results indicate that SEGMA is producing very compact gate-matrix layouts.

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