53 research outputs found
Structure and Complexity in Planning with Unary Operators
Unary operator domains -- i.e., domains in which operators have a single
effect -- arise naturally in many control problems. In its most general form,
the problem of STRIPS planning in unary operator domains is known to be as hard
as the general STRIPS planning problem -- both are PSPACE-complete. However,
unary operator domains induce a natural structure, called the domain's causal
graph. This graph relates between the preconditions and effect of each domain
operator. Causal graphs were exploited by Williams and Nayak in order to
analyze plan generation for one of the controllers in NASA's Deep-Space One
spacecraft. There, they utilized the fact that when this graph is acyclic, a
serialization ordering over any subgoal can be obtained quickly. In this paper
we conduct a comprehensive study of the relationship between the structure of a
domain's causal graph and the complexity of planning in this domain. On the
positive side, we show that a non-trivial polynomial time plan generation
algorithm exists for domains whose causal graph induces a polytree with a
constant bound on its node indegree. On the negative side, we show that even
plan existence is hard when the graph is a directed-path singly connected DAG.
More generally, we show that the number of paths in the causal graph is closely
related to the complexity of planning in the associated domain. Finally we
relate our results to the question of complexity of planning with serializable
subgoals
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
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
Information about user preferences plays a key role in automated decision
making. In many domains it is desirable to assess such preferences in a
qualitative rather than quantitative way. In this paper, we propose a
qualitative graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics
for this model, and describe how the structure of the network can be exploited
in several inference tasks, such as determining whether one outcome dominates
(is preferred to) another, ordering a set outcomes according to the preference
relation, and constructing the best outcome subject to available evidence
On Graphical Modeling of Preference and Importance
In recent years, CP-nets have emerged as a useful tool for supporting
preference elicitation, reasoning, and representation. CP-nets capture and
support reasoning with qualitative conditional preference statements,
statements that are relatively natural for users to express. In this paper, we
extend the CP-nets formalism to handle another class of very natural
qualitative statements one often uses in expressing preferences in daily life -
statements of relative importance of attributes. The resulting formalism,
TCP-nets, maintains the spirit of CP-nets, in that it remains focused on using
only simple and natural preference statements, uses the ceteris paribus
semantics, and utilizes a graphical representation of this information to
reason about its consistency and to perform, possibly constrained, optimization
using it. The extra expressiveness it provides allows us to better model
tradeoffs users would like to make, more faithfully representing their
preferences
Conditional Preference Nets and Possibilistic Logic
International audienceCP-nets (Conditional preference networks) are a well-known compact graphical representation of preferences in Artificial Intelligence, that can be viewed as a qualitative counterpart to Bayesian nets. In case of binary attributes it captures specific partial orderings over Boolean interpretations where strict preference statements are defined between interpretations which differ by a single flip of an attribute value. It respects preferential independence encoded by the ceteris paribus property. The popularity of this approach has motivated some comparison with other preference representation setting such as possibilistic logic. In this paper, we focus our discussion on the possibilistic representation of CP-nets, and the question whether it is possible to capture the CP-net partial order over interpretations by means of a possibilistic knowledge base and a suitable semantics. We show that several results in the literature on the alleged faithful representation of CP-nets by possibilistic bases are questionable. To this aim we discuss some canonical examples of CP-net topologies where the considered possibilistic approach fails to exactly capture the partial order induced by CP-nets, thus shedding light on the difficulties encountered when trying to reconcile the two frameworks
An extension of SPARQL for expressing qualitative preferences
In this paper we present SPREFQL, an extension of the SPARQL language that
allows appending a PREFER clause that expresses "soft" preferences over the
query results obtained by the main body of the query. The extension does not
add expressivity and any SPREFQL query can be transformed to an equivalent
standard SPARQL query. However, clearly separating preferences from the "hard"
patterns and filters in the WHERE clause gives queries where the intention of
the client is more cleanly expressed, an advantage for both human readability
and machine optimization. In the paper we formally define the syntax and the
semantics of the extension and we also provide empirical evidence that
optimizations specific to SPREFQL improve run-time efficiency by comparison to
the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna,
October 201
A Comparison of the Notions of Optimality in Soft Constraints and Graphical Games
The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of two formalisms used for different purposes and in different research areas: graphical games and soft constraints. We relate the notion of optimality used in the area of soft constraint satisfaction problems (SCSPs) to that used in graphical games, showing that for a large class of SCSPs that includes weighted constraints every optimal solution corresponds to a Nash equilibrium that is also a Pareto efficient joint strategy
Priority-Based Human Resource Allocation in Business Processes
In Business Process Management Systems, human resource management typically covers two steps: resource assignment at design time and resource allocation at run time. Although concepts like rolebased assignment often yield several potential performers for an activity, there is a lack of mechanisms for prioritizing them, e.g., according to their skills or current workload. in this paper, we address this research gap. More specifically, we introduce an approach to define resource preferences grounded on a validated, generic user preference model initially developed for semantic web services. Furthermore, we show an implementation of the approach demonstrating its feasibility. Keywords: preference modeling, preference resolution, priority-based allocation, priority ranking, RAL, resource allocation, SOUP
Asynchronous simulation of Boolean networks by monotone Boolean networks
International audienceWe prove that the fully asynchronous dynamics of a Boolean network f : {0, 1}^n → {0, 1}^n without negative loop can be simulated, in a very specific way, by a monotone Boolean network with 2n components. We then use this result to prove that, for every even n, there exists a monotone Boolean network f : {0, 1}^n → {0, 1}^n , an initial configuration x and a fixed point y of f such that: (i) y can be reached from x with a fully asynchronous updating strategy, and (ii) all such strategies contains at least 2^{n/2} updates. This contrasts with the following known property: if f : {0, 1}^n → {0, 1}^n is monotone, then, for every initial configuration x, there exists a fixed point y such that y can be reached from x with a fully asynchronous strategy that contains at most n updates
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