Jorg Hoffmann

AAAI Conference 2020 Conference Paper

Let’s Learn Their Language? A Case for Planning with Automata-Network Languages from Model Checking

• Jorg Hoffmann
• Holger Hermanns
• Michaela Klauck
• Marcel Steinmetz
• Erez Karpas
• Daniele Magazzeni

It is widely known that AI planning and model checking are closely related. Compilations have been devised between various pairs of language fragments. What has barely been voiced yet, though, is the idea to let go of one’s own modeling language, and use one from the other area instead. We advocate that idea here – to use automata-network languages from model checking instead of PDDL – motivated by modeling difficulties relating to planning agents surrounded by exogenous agents in complex environments. One could, of course, address this by designing additional extended planning languages. But one can also leverage decades of work on modeling in the formal methods community, creating potential for deep synergy and integration with their techniques as a side effect. We believe there’s a case to be made for the latter, as one modeling alternative in planning among others.

AAAI Conference 2019 Conference Paper

Real-Time Planning as Decision-Making under Uncertainty

• Andrew Mitchell
• Wheeler Ruml
• Fabian Spaniol
• Jorg Hoffmann
• Marek Petrik

In real-time planning, an agent must select the next action to take within a fixed time bound. Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this paper, we reconsider real-time planning as a problem of decision-making under uncertainty. We propose treating heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly metareasoning about this uncertainty.

IJCAI Conference 2003 Conference Paper

In Defense of PDDL Axioms

• Sylvie Thiibaux
• Jorg Hoffmann
• Bernhard Nebel

IJCAI Conference 1999 Conference Paper

A New Method to Index and Query Sets

• Jorg Hoffmann
• Jana Koehler

Let us consider the following problem: Given a (probably huge) set of sets S and a query set g, is there some set s S such that This problem occurs in at least four application areas: the match­ ing of a large number (usually several 100, 000s) of production rules, the pro­ cessing of queries in data bases support­ ing set-valued attributes, the identifica­ tion of inconsistent subgoals during ar­ tificial intelligence planning and the de­ tection of potential periodic chains in la­ beled tableau systems for modal logics. In this paper, we introduce a data struc­ ture and algorithm that allow a com­ pact representation of such a huge set of sets and an efficient answering of subset and superset queries. The algorithm has been used successfully in the IPP system and enabled this planner to win the ADL track of the first planning competition.