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OBDDbased Universal Planning for Synchronized Agents in NonDeterministic Domains
 Journal of Artificial Intelligence Research
, 2000
"... Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to nondeterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (obdds) to encode a planning domain as a nondeterministic finite automaton ..."
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Cited by 64 (20 self)
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Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to nondeterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (obdds) to encode a planning domain as a nondeterministic finite automaton and then apply fast algorithms from model checking to search for a solution. obdds can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in nondeterministic, multiagent domains. In this article, we present umop, a new universal obddbased planning framework for nondeterministic, multiagent domains. We introduce a new planning domain description language, NADL, to specify nondeterministic, multiagent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to bu...
CONFORMANT PLANNING VIA MODEL CHECKING
, 1999
"... Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal for any possible initial state and nondeterministic behavior of the planning domain. In this paper we present a new approach to conformant planning. We propose an algorithm that returns the se ..."
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Cited by 52 (4 self)
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Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal for any possible initial state and nondeterministic behavior of the planning domain. In this paper we present a new approach to conformant planning. We propose an algorithm that returns the set of all conformant plans of minimal length if the problem admits a solution, otherwise it returns with failure. Our work is based on the planning via model checking paradigm, and relies on symbolic techniques such as Binary Decision Diagrams to compactly represent and eciently analyze the planning domain. The algorithm, called cmbp, has been implemented in the mbp planner. cmbp is strictly more expressive than the state of the art conformant planner cgp. Furthermore, an experimental evaluation suggests that cmbp is able to deal with uncertainties more efficiently than cgp.
LogicBased Subsumption Architecture
 IN PROC. SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI '99)
, 1999
"... We describe a logicbased AI architecture based on Brooks' subsumption architecture. In this architecture, we axiomatize dierent layers of control in FirstOrder Logic (FOL) and use independent theorem provers to derive each layer's outputs given its inputs. We implement the subsumpt ..."
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Cited by 21 (9 self)
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We describe a logicbased AI architecture based on Brooks' subsumption architecture. In this architecture, we axiomatize dierent layers of control in FirstOrder Logic (FOL) and use independent theorem provers to derive each layer's outputs given its inputs. We implement the subsumption of lower layers by higher layers using circumscription to make assumptions in lower layers, and nonmonotonically retract them when higher layers draw new conclusions. We also
The Fluent Calculus  A Specification Language for Robots with Sensors in Nondeterministic, Concurrent, and Ramifying Environments
"... The Fluent Calculus is presented as a comprehensive specification and programming language for endowing robots with the ability of task planning in complex environments. Based on a solution to the classical Frame Problem in pure firstorder logic, our calculus allows to solve planning problems where ..."
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Cited by 9 (3 self)
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The Fluent Calculus is presented as a comprehensive specification and programming language for endowing robots with the ability of task planning in complex environments. Based on a solution to the classical Frame Problem in pure firstorder logic, our calculus allows to solve planning problems where the robot has incomplete state knowledge and which involve the use of sensors, actions with uncertain effects, actions with ramications (i.e., indirect effects), and the concurrent execution of actions. Our new theory of sensing is distinguished by its simple inference scheme for calculating the effects of actions on state knowledge and by its comparatively simple account of nonknowledge. A realization of the Fluent Calculus by means of constraint logic programming is presented. Outstanding novel features of the system are to solve the inferential Frame Problem under incomplete state information, to allow for solving planning problems with knowledge goals, and to combine nondeterminism, concurrency, and ramification.
Reasoning about Actions in
"... In this paper we present a theory for reasoning about actions which is based on Dynamic Linear Time Temporal Logic (DLTL). DLTL is a simple extension of propositional temporal logic of linear time in which regular programs of propositional dynamic logic can be used for indexing temporal modalities. ..."
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In this paper we present a theory for reasoning about actions which is based on Dynamic Linear Time Temporal Logic (DLTL). DLTL is a simple extension of propositional temporal logic of linear time in which regular programs of propositional dynamic logic can be used for indexing temporal modalities. The action theory we de ne allows to reason with incomplete initial states, to do postdiction and to deal with rami cations and with nondeterministic actions, which are captured by possibly alternative extensions (temporal models). The expressiveness of temporal logic is exploited to enhance the action language by allowing the de nition of general temporal constraints as well as complex actions in the speci cation of the domain description. We show that the temporal projection problem and the planning problem can be modelled as satis ability problems in DLTL.