364 research outputs found
Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting
We present a new algorithm for probabilistic planning with no observability.
Our algorithm, called Probabilistic-FF, extends the heuristic forward-search
machinery of Conformant-FF to problems with probabilistic uncertainty about
both the initial state and action effects. Specifically, Probabilistic-FF
combines Conformant-FFs techniques with a powerful machinery for weighted model
counting in (weighted) CNFs, serving to elegantly define both the search space
and the heuristic function. Our evaluation of Probabilistic-FF shows its fine
scalability in a range of probabilistic domains, constituting a several orders
of magnitude improvement over previous results in this area. We use a
problematic case to point out the main open issue to be addressed by further
research
05171 Abstracts Collection -- Nonmonotonic Reasoning, Answer Set Programming and Constraints
From 24.04.05 to 29.04.05, the Dagstuhl Seminar
05171 ``Nonmonotonic Reasoning, Answer Set Programming and Constraints\u27\u27
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Reasoning about Actions with Temporal Answer Sets
In this paper we combine Answer Set Programming (ASP) with Dynamic Linear
Time Temporal Logic (DLTL) to define a temporal logic programming language for
reasoning about complex actions and infinite computations. DLTL extends
propositional temporal logic of linear time with regular programs of
propositional dynamic logic, which are used for indexing temporal modalities.
The action language allows general DLTL formulas to be included in domain
descriptions to constrain the space of possible extensions. We introduce a
notion of Temporal Answer Set for domain descriptions, based on the usual
notion of Answer Set. Also, we provide a translation of domain descriptions
into standard ASP and we use Bounded Model Checking techniques for the
verification of DLTL constraints.Comment: To appear in Theory and Practice of Logic Programmin
A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information
We present a state-based regression function for planning domains where an
agent does not have complete information and may have sensing actions. We
consider binary domains and employ a three-valued characterization of domains
with sensing actions to define the regression function. We prove the soundness
and completeness of our regression formulation with respect to the definition
of progression. More specifically, we show that (i) a plan obtained through
regression for a planning problem is indeed a progression solution of that
planning problem, and that (ii) for each plan found through progression, using
regression one obtains that plan or an equivalent one.Comment: 34 pages, 7 Figure
Prediction Process in Multi-Agent System Online Monitoring: Centralized and Distributed Approaches
This paper discusses the prediction process, which is the main step of the online monitoring process for a multi-agent plan. The monitoring process uses a relational model to estimate the internal status of the system, which is dynamic (changes over time). Unfortunately, the agents have partial observability of the environment; thus, the monitoring process cannot accurately determine the system status (known in the literature as belief state) at any instant. The prediction process is composed of two stages: a simulation stage (prediction of all possible system states at the succeeding time) and a clipping stage (elimination of states that are incompatible with the observations or with the constraints from predicted system states)
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