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1109.5714 | N. Samaras | N. Samaras, K. Stergiou | Binary Encodings of Non-binary Constraint Satisfaction Problems:
Algorithms and Experimental Results | null | Journal Of Artificial Intelligence Research, Volume 24, pages
641-684, 2005 | 10.1613/jair.1776 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A non-binary Constraint Satisfaction Problem (CSP) can be solved directly
using extended versions of binary techniques. Alternatively, the non-binary
problem can be translated into an equivalent binary one. In this case, it is
generally accepted that the translated problem can be solved by applying
well-established techniques for binary CSPs. In this paper we evaluate the
applicability of the latter approach. We demonstrate that the use of standard
techniques for binary CSPs in the encodings of non-binary problems is
problematic and results in models that are very rarely competitive with the
non-binary representation. To overcome this, we propose specialized arc
consistency and search algorithms for binary encodings, and we evaluate them
theoretically and empirically. We consider three binary representations; the
hidden variable encoding, the dual encoding, and the double encoding.
Theoretical and empirical results show that, for certain classes of non-binary
constraints, binary encodings are a competitive option, and in many cases, a
better one than the non-binary representation.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 20:23:01 GMT"
}
] | 1,317,168,000,000 | [
[
"Samaras",
"N.",
""
],
[
"Stergiou",
"K.",
""
]
] |
1109.5716 | P. Adjiman | P. Adjiman, P. Chatalic, F. Goasdoue, M. C. Rousset, L. Simon | Distributed Reasoning in a Peer-to-Peer Setting: Application to the
Semantic Web | null | Journal Of Artificial Intelligence Research, Volume 25, pages
269-314, 2006 | 10.1613/jair.1785 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 20:23:24 GMT"
}
] | 1,317,168,000,000 | [
[
"Adjiman",
"P.",
""
],
[
"Chatalic",
"P.",
""
],
[
"Goasdoue",
"F.",
""
],
[
"Rousset",
"M. C.",
""
],
[
"Simon",
"L.",
""
]
] |
1109.5717 | H. H. Hoos | H. H. Hoos, W. Pullan | Dynamic Local Search for the Maximum Clique Problem | null | Journal Of Artificial Intelligence Research, Volume 25, pages
159-185, 2006 | 10.1613/jair.1815 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce DLS-MC, a new stochastic local search algorithm
for the maximum clique problem. DLS-MC alternates between phases of iterative
improvement, during which suitable vertices are added to the current clique,
and plateau search, during which vertices of the current clique are swapped
with vertices not contained in the current clique. The selection of vertices is
solely based on vertex penalties that are dynamically adjusted during the
search, and a perturbation mechanism is used to overcome search stagnation. The
behaviour of DLS-MC is controlled by a single parameter, penalty delay, which
controls the frequency at which vertex penalties are reduced. We show
empirically that DLS-MC achieves substantial performance improvements over
state-of-the-art algorithms for the maximum clique problem over a large range
of the commonly used DIMACS benchmark instances.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 20:24:56 GMT"
}
] | 1,317,168,000,000 | [
[
"Hoos",
"H. H.",
""
],
[
"Pullan",
"W.",
""
]
] |
1109.5732 | G. Gutnik | G. Gutnik, G. A. Kaminka | Representing Conversations for Scalable Overhearing | null | Journal Of Artificial Intelligence Research, Volume 25, pages
349-387, 2006 | 10.1613/jair.1829 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent applications. Recently, Petri nets have been shown to be an
interesting approach to such representation, and radically different approaches
using Petri nets have been proposed. However, their relative strengths and
weaknesses have not been examined. Moreover, their scalability and suitability
for different tasks have not been addressed. This paper addresses both these
challenges. First, we analyze existing Petri net representations in terms of
their scalability and appropriateness for overhearing, an important task in
monitoring open multi-agent systems. Then, building on the insights gained, we
introduce a novel representation using Colored Petri nets that explicitly
represent legal joint conversation states and messages. This representation
approach offers significant improvements in scalability and is particularly
suitable for overhearing. Furthermore, we show that this new representation
offers a comprehensive coverage of all conversation features of FIPA
conversation standards. We also present a procedure for transforming AUML
conversation protocol diagrams (a standard human-readable representation), to
our Colored Petri net representation.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 21:56:16 GMT"
}
] | 1,317,168,000,000 | [
[
"Gutnik",
"G.",
""
],
[
"Kaminka",
"G. A.",
""
]
] |
1109.5750 | P. Haslum | P. Haslum | Improving Heuristics Through Relaxed Search - An Analysis of TP4 and
HSP*a in the 2004 Planning Competition | null | Journal Of Artificial Intelligence Research, Volume 25, pages
233-267, 2006 | 10.1613/jair.1885 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The hm admissible heuristics for (sequential and temporal) regression
planning are defined by a parameterized relaxation of the optimal cost function
in the regression search space, where the parameter m offers a trade-off
between the accuracy and computational cost of theheuristic. Existing methods
for computing the hm heuristic require time exponential in m, limiting them to
small values (m andlt= 2). The hm heuristic can also be viewed as the optimal
cost function in a relaxation of the search space: this paper presents relaxed
search, a method for computing this function partially by searching in the
relaxed space. The relaxed search method, because it computes hm only
partially, is computationally cheaper and therefore usable for higher values of
m. The (complete) hm heuristic is combined with partial hm heuristics, for m =
3,..., computed by relaxed search, resulting in a more accurate heuristic.
This use of the relaxed search method to improve on the hm heuristic is
evaluated by comparing two optimal temporal planners: TP4, which does not use
it, and HSP*a, which uses it but is otherwise identical to TP4. The comparison
is made on the domains used in the 2004 International Planning Competition, in
which both planners participated. Relaxed search is found to be cost effective
in some of these domains, but not all. Analysis reveals a characterization of
the domains in which relaxed search can be expected to be cost effective, in
terms of two measures on the original and relaxed search spaces. In the domains
where relaxed search is cost effective, expanding small states is
computationally cheaper than expanding large states and small states tend to
have small successor states.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 00:19:20 GMT"
}
] | 1,317,168,000,000 | [
[
"Haslum",
"P.",
""
]
] |
1109.5920 | Emmanuel Hebrard | Diarmuid Grimes (4C UCC), Emmanuel Hebrard (LAAS) | Models and Strategies for Variants of the Job Shop Scheduling Problem | Principles and Practice of Constraint Programming - CP 2011, Perugia
: Italy (2011) | null | 10.1007/978-3-642-23786-7 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorably to more classical constraint programming methods relying on
propagation algorithms for global unary or cumulative resource constraints and
dedicated search heuristics. In particular, we described an approach that
combines restarting, with a generic adaptive heuristic and solution guided
branching on a simple model based on a decomposition of disjunctive
constraints. In this paper, we introduce an adaptation of this technique for an
important subclass of job shop scheduling problems (JSPs), where the objective
function involves minimization of earliness/tardiness costs. We further show
that our technique can be improved by adding domain specific information for
one variant of the JSP (involving time lag constraints). In particular we
introduce a dedicated greedy heuristic, and an improved model for the case
where the maximal time lag is 0 (also referred to as no-wait JSPs).
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 14:53:01 GMT"
}
] | 1,317,168,000,000 | [
[
"Grimes",
"Diarmuid",
"",
"4C UCC"
],
[
"Hebrard",
"Emmanuel",
"",
"LAAS"
]
] |
1109.5951 | Shane Legg Dr | Shane Legg, Joel Veness | An Approximation of the Universal Intelligence Measure | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Universal Intelligence Measure is a recently proposed formal definition
of intelligence. It is mathematically specified, extremely general, and
captures the essence of many informal definitions of intelligence. It is based
on Hutter's Universal Artificial Intelligence theory, an extension of Ray
Solomonoff's pioneering work on universal induction. Since the Universal
Intelligence Measure is only asymptotically computable, building a practical
intelligence test from it is not straightforward. This paper studies the
practical issues involved in developing a real-world UIM-based performance
metric. Based on our investigation, we develop a prototype implementation which
we use to evaluate a number of different artificial agents.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 16:09:27 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Sep 2011 21:38:56 GMT"
}
] | 1,317,600,000,000 | [
[
"Legg",
"Shane",
""
],
[
"Veness",
"Joel",
""
]
] |
1109.6029 | S. Schroedl | S. Schroedl | An Improved Search Algorithm for Optimal Multiple-Sequence Alignment | null | Journal Of Artificial Intelligence Research, Volume 23, pages
587-623, 2005 | 10.1613/jair.1534 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multiple sequence alignment (MSA) is a ubiquitous problem in computational
biology. Although it is NP-hard to find an optimal solution for an arbitrary
number of sequences, due to the importance of this problem researchers are
trying to push the limits of exact algorithms further. Since MSA can be cast as
a classical path finding problem, it is attracting a growing number of AI
researchers interested in heuristic search algorithms as a challenge with
actual practical relevance. In this paper, we first review two previous,
complementary lines of research. Based on Hirschbergs algorithm, Dynamic
Programming needs O(kN^(k-1)) space to store both the search frontier and the
nodes needed to reconstruct the solution path, for k sequences of length N.
Best first search, on the other hand, has the advantage of bounding the search
space that has to be explored using a heuristic. However, it is necessary to
maintain all explored nodes up to the final solution in order to prevent the
search from re-expanding them at higher cost. Earlier approaches to reduce the
Closed list are either incompatible with pruning methods for the Open list, or
must retain at least the boundary of the Closed list. In this article, we
present an algorithm that attempts at combining the respective advantages; like
A* it uses a heuristic for pruning the search space, but reduces both the
maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The
underlying idea is to conduct a series of searches with successively increasing
upper bounds, but using the DP ordering as the key for the Open priority queue.
With a suitable choice of thresholds, in practice, a running time below four
times that of A* can be expected. In our experiments we show that our algorithm
outperforms one of the currently most successful algorithms for optimal
multiple sequence alignments, Partial Expansion A*, both in time and memory.
Moreover, we apply a refined heuristic based on optimal alignments not only of
pairs of sequences, but of larger subsets. This idea is not new; however, to
make it practically relevant we show that it is equally important to bound the
heuristic computation appropriately, or the overhead can obliterate any
possible gain. Furthermore, we discuss a number of improvements in time and
space efficiency with regard to practical implementations. Our algorithm, used
in conjunction with higher-dimensional heuristics, is able to calculate for the
first time the optimal alignment for almost all of the problems in Reference 1
of the benchmark database BAliBASE.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 20:40:06 GMT"
}
] | 1,317,254,400,000 | [
[
"Schroedl",
"S.",
""
]
] |
1109.6030 | M. Beetz | M. Beetz, H. Grosskreutz | Probabilistic Hybrid Action Models for Predicting Concurrent
Percept-driven Robot Behavior | null | Journal Of Artificial Intelligence Research, Volume 24, pages
799-849, 2005 | 10.1613/jair.1565 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 20:41:47 GMT"
}
] | 1,317,254,400,000 | [
[
"Beetz",
"M.",
""
],
[
"Grosskreutz",
"H.",
""
]
] |
1109.6033 | G. DeJong | G. DeJong, A. Epshteyn | Generative Prior Knowledge for Discriminative Classification | null | Journal Of Artificial Intelligence Research, Volume 27, pages
25-53, 2006 | 10.1613/jair.1934 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel framework for integrating prior knowledge into
discriminative classifiers. Our framework allows discriminative classifiers
such as Support Vector Machines (SVMs) to utilize prior knowledge specified in
the generative setting. The dual objective of fitting the data and respecting
prior knowledge is formulated as a bilevel program, which is solved
(approximately) via iterative application of second-order cone programming. To
test our approach, we consider the problem of using WordNet (a semantic
database of English language) to improve low-sample classification accuracy of
newsgroup categorization. WordNet is viewed as an approximate, but readily
available source of background knowledge, and our framework is capable of
utilizing it in a flexible way.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 20:53:29 GMT"
}
] | 1,317,254,400,000 | [
[
"DeJong",
"G.",
""
],
[
"Epshteyn",
"A.",
""
]
] |
1109.6051 | M. Helmert | M. Helmert | The Fast Downward Planning System | null | Journal Of Artificial Intelligence Research, Volume 26, pages
191-246, 2006 | 10.1613/jair.1705 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fast Downward is a classical planning system based on heuristic search. It
can deal with general deterministic planning problems encoded in the
propositional fragment of PDDL2.2, including advanced features like ADL
conditions and effects and derived predicates (axioms). Like other well-known
planners such as HSP and FF, Fast Downward is a progression planner, searching
the space of world states of a planning task in the forward direction. However,
unlike other PDDL planning systems, Fast Downward does not use the
propositional PDDL representation of a planning task directly. Instead, the
input is first translated into an alternative representation called
multi-valued planning tasks, which makes many of the implicit constraints of a
propositional planning task explicit. Exploiting this alternative
representation, Fast Downward uses hierarchical decompositions of planning
tasks for computing its heuristic function, called the causal graph heuristic,
which is very different from traditional HSP-like heuristics based on ignoring
negative interactions of operators.
In this article, we give a full account of Fast Downwards approach to solving
multi-valued planning tasks. We extend our earlier discussion of the causal
graph heuristic to tasks involving axioms and conditional effects and present
some novel techniques for search control that are used within Fast Downwards
best-first search algorithm: preferred operators transfer the idea of helpful
actions from local search to global best-first search, deferred evaluation of
heuristic functions mitigates the negative effect of large branching factors on
search performance, and multi-heuristic best-first search combines several
heuristic evaluation functions within a single search algorithm in an
orthogonal way. We also describe efficient data structures for fast state
expansion (successor generators and axiom evaluators) and present a new
non-heuristic search algorithm called focused iterative-broadening search,
which utilizes the information encoded in causal graphs in a novel way.
Fast Downward has proven remarkably successful: It won the "classical (i.e.,
propositional, non-optimising) track of the 4th International Planning
Competition at ICAPS 2004, following in the footsteps of planners such as FF
and LPG. Our experiments show that it also performs very well on the benchmarks
of the earlier planning competitions and provide some insights about the
usefulness of the new search enhancements.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 22:04:43 GMT"
}
] | 1,317,254,400,000 | [
[
"Helmert",
"M.",
""
]
] |
1109.6052 | V. R. Lesser | V. R. Lesser, R. Mailler | Asynchronous Partial Overlay: A New Algorithm for Solving Distributed
Constraint Satisfaction Problems | null | Journal Of Artificial Intelligence Research, Volume 25, pages
529-576, 2006 | 10.1613/jair.1786 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed Constraint Satisfaction (DCSP) has long been considered an
important problem in multi-agent systems research. This is because many
real-world problems can be represented as constraint satisfaction and these
problems often present themselves in a distributed form. In this article, we
present a new complete, distributed algorithm called Asynchronous Partial
Overlay (APO) for solving DCSPs that is based on a cooperative mediation
process. The primary ideas behind this algorithm are that agents, when acting
as a mediator, centralize small, relevant portions of the DCSP, that these
centralized subproblems overlap, and that agents increase the size of their
subproblems along critical paths within the DCSP as the problem solving
unfolds. We present empirical evidence that shows that APO outperforms other
known, complete DCSP techniques.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2011 22:05:46 GMT"
}
] | 1,317,254,400,000 | [
[
"Lesser",
"V. R.",
""
],
[
"Mailler",
"R.",
""
]
] |
1109.6344 | R. Booth | R. Booth, T. Meyer | Admissible and Restrained Revision | null | Journal Of Artificial Intelligence Research, Volume 26, pages
127-151, 2006 | 10.1613/jair.1874 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As partial justification of their framework for iterated belief revision
Darwiche and Pearl convincingly argued against Boutiliers natural revision and
provided a prototypical revision operator that fits into their scheme. We show
that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller
class of operators which we refer to as admissible. Admissible revision ensures
that the penultimate input is not ignored completely, thereby eliminating
natural revision, but includes the Darwiche-Pearl operator, Nayaks
lexicographic revision operator, and a newly introduced operator called
restrained revision. We demonstrate that restrained revision is the most
conservative of admissible revision operators, effecting as few changes as
possible, while lexicographic revision is the least conservative, and point out
that restrained revision can also be viewed as a composite operator, consisting
of natural revision preceded by an application of a "backwards revision"
operator previously studied by Papini. Finally, we propose the establishment of
a principled approach for choosing an appropriate revision operator in
different contexts and discuss future work.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2011 20:26:49 GMT"
}
] | 1,317,340,800,000 | [
[
"Booth",
"R.",
""
],
[
"Meyer",
"T.",
""
]
] |
1109.6345 | R. I. Brafman | R. I. Brafman, C. Domshlak, S. E. Shimony | On Graphical Modeling of Preference and Importance | null | Journal Of Artificial Intelligence Research, Volume 25, pages
389-424, 2006 | 10.1613/jair.1895 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 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.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2011 20:29:41 GMT"
}
] | 1,317,340,800,000 | [
[
"Brafman",
"R. I.",
""
],
[
"Domshlak",
"C.",
""
],
[
"Shimony",
"S. E.",
""
]
] |
1109.6346 | M. Pistore | M. Pistore, M. Y. Vardi | The Planning Spectrum - One, Two, Three, Infinity | null | Journal Of Artificial Intelligence Research, Volume 30, pages
101-132, 2007 | 10.1613/jair.1909 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linear Temporal Logic (LTL) is widely used for defining conditions on the
execution paths of dynamic systems. In the case of dynamic systems that allow
for nondeterministic evolutions, one has to specify, along with an LTL formula
f, which are the paths that are required to satisfy the formula. Two extreme
cases are the universal interpretation A.f, which requires that the formula be
satisfied for all execution paths, and the existential interpretation E.f,
which requires that the formula be satisfied for some execution path.
When LTL is applied to the definition of goals in planning problems on
nondeterministic domains, these two extreme cases are too restrictive. It is
often impossible to develop plans that achieve the goal in all the
nondeterministic evolutions of a system, and it is too weak to require that the
goal is satisfied by some execution.
In this paper we explore alternative interpretations of an LTL formula that
are between these extreme cases. We define a new language that permits an
arbitrary combination of the A and E quantifiers, thus allowing, for instance,
to require that each finite execution can be extended to an execution
satisfying an LTL formula (AE.f), or that there is some finite execution whose
extensions all satisfy an LTL formula (EA.f). We show that only eight of these
combinations of path quantifiers are relevant, corresponding to an alternation
of the quantifiers of length one (A and E), two (AE and EA), three (AEA and
EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for
the new language that is based on an automata-theoretic approach, and study its
complexity.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2011 20:35:31 GMT"
}
] | 1,317,340,800,000 | [
[
"Pistore",
"M.",
""
],
[
"Vardi",
"M. Y.",
""
]
] |
1109.6348 | A. Roy | A. Roy | Fault Tolerant Boolean Satisfiability | null | Journal Of Artificial Intelligence Research, Volume 25, pages
503-527, 2006 | 10.1613/jair.1914 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A delta-model is a satisfying assignment of a Boolean formula for which any
small alteration, such as a single bit flip, can be repaired by flips to some
small number of other bits, yielding a new satisfying assignment. These
satisfying assignments represent robust solutions to optimization problems
(e.g., scheduling) where it is possible to recover from unforeseen events
(e.g., a resource becoming unavailable). The concept of delta-models was
introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that
finding delta-models for general Boolean formulas is NP-complete. In this
paper, we extend that result by studying the complexity of finding delta-models
for classes of Boolean formulas which are known to have polynomial time
satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT,
dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the
complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have
polynomial time tests for delta-models, testing whether a Horn-SAT formula has
one is NP-complete.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2011 20:37:53 GMT"
}
] | 1,317,340,800,000 | [
[
"Roy",
"A.",
""
]
] |
1109.6361 | J. Y. Chai | J. Y. Chai, Z. Prasov, S. Qu | Cognitive Principles in Robust Multimodal Interpretation | null | Journal Of Artificial Intelligence Research, Volume 27, pages
55-83, 2006 | 10.1613/jair.1936 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal conversational interfaces provide a natural means for users to
communicate with computer systems through multiple modalities such as speech
and gesture. To build effective multimodal interfaces, automated interpretation
of user multimodal inputs is important. Inspired by the previous investigation
on cognitive status in multimodal human machine interaction, we have developed
a greedy algorithm for interpreting user referring expressions (i.e.,
multimodal reference resolution). This algorithm incorporates the cognitive
principles of Conversational Implicature and Givenness Hierarchy and applies
constraints from various sources (e.g., temporal, semantic, and contextual) to
resolve references. Our empirical results have shown the advantage of this
algorithm in efficiently resolving a variety of user references. Because of its
simplicity and generality, this approach has the potential to improve the
robustness of multimodal input interpretation.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2011 21:45:34 GMT"
}
] | 1,317,340,800,000 | [
[
"Chai",
"J. Y.",
""
],
[
"Prasov",
"Z.",
""
],
[
"Qu",
"S.",
""
]
] |
1109.6618 | D. Davidov | D. Davidov, S. Markovitch | Multiple-Goal Heuristic Search | null | Journal Of Artificial Intelligence Research, Volume 26, pages
417-451, 2006 | 10.1613/jair.1940 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources. We
show the inadequacy of traditional distance-estimation heuristics for tasks of
this type and present alternative heuristics that are more appropriate for
multiple-goal search. In particular, we introduce the marginal-utility
heuristic, which estimates the cost and the benefit of exploring a subtree
below a search node. We developed two methods for online learning of the
marginal-utility heuristic. One is based on local similarity of the partial
marginal utility of sibling nodes, and the other generalizes marginal-utility
over the state feature space. We apply our adaptive and non-adaptive
multiple-goal search algorithms to several problems, including focused
crawling, and show their superiority over existing methods.
| [
{
"version": "v1",
"created": "Thu, 29 Sep 2011 18:50:13 GMT"
}
] | 1,426,723,200,000 | [
[
"Davidov",
"D.",
""
],
[
"Markovitch",
"S.",
""
]
] |
1109.6621 | S. Hoelldobler | S. Hoelldobler, E. Karabaev, O. Skvortsova | FluCaP: A Heuristic Search Planner for First-Order MDPs | null | Journal Of Artificial Intelligence Research, Volume 27, pages
419-439, 2006 | 10.1613/jair.1965 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a heuristic search algorithm for solving first-order Markov
Decision Processes (FOMDPs). Our approach combines first-order state
abstraction that avoids evaluating states individually, and heuristic search
that avoids evaluating all states. Firstly, in contrast to existing systems,
which start with propositionalizing the FOMDP and then perform state
abstraction on its propositionalized version we apply state abstraction
directly on the FOMDP avoiding propositionalization. This kind of abstraction
is referred to as first-order state abstraction. Secondly, guided by an
admissible heuristic, the search is restricted to those states that are
reachable from the initial state. We demonstrate the usefulness of the above
techniques for solving FOMDPs with a system, referred to as FluCaP (formerly,
FCPlanner), that entered the probabilistic track of the 2004 International
Planning Competition (IPC2004) and demonstrated an advantage over other
planners on the problems represented in first-order terms.
| [
{
"version": "v1",
"created": "Thu, 29 Sep 2011 18:58:54 GMT"
}
] | 1,317,340,800,000 | [
[
"Hoelldobler",
"S.",
""
],
[
"Karabaev",
"E.",
""
],
[
"Skvortsova",
"O.",
""
]
] |
1109.6841 | Percy Liang | Percy Liang and Michael I. Jordan and Dan Klein | Learning Dependency-Based Compositional Semantics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical form and computing the answer given a
structured database of facts. The core part of such a system is the semantic
parser that maps questions to logical forms. Semantic parsers are typically
trained from examples of questions annotated with their target logical forms,
but this type of annotation is expensive.
Our goal is to learn a semantic parser from question-answer pairs instead,
where the logical form is modeled as a latent variable. Motivated by this
challenging learning problem, we develop a new semantic formalism,
dependency-based compositional semantics (DCS), which has favorable linguistic,
statistical, and computational properties. We define a log-linear distribution
over DCS logical forms and estimate the parameters using a simple procedure
that alternates between beam search and numerical optimization. On two standard
semantic parsing benchmarks, our system outperforms all existing
state-of-the-art systems, despite using no annotated logical forms.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 14:49:30 GMT"
}
] | 1,317,600,000,000 | [
[
"Liang",
"Percy",
""
],
[
"Jordan",
"Michael I.",
""
],
[
"Klein",
"Dan",
""
]
] |
1110.0020 | A. C. Cem Say | \"O. Y{\i}lmaz, A. C. C. Say | Causes of Ineradicable Spurious Predictions in Qualitative Simulation | null | Journal Of Artificial Intelligence Research, Volume 27, pages
551-575, 2006 | 10.1613/jair.2065 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It was recently proved that a sound and complete qualitative simulator does
not exist, that is, as long as the input-output vocabulary of the
state-of-the-art QSIM algorithm is used, there will always be input models
which cause any simulator with a coverage guarantee to make spurious
predictions in its output. In this paper, we examine whether a meaningfully
expressive restriction of this vocabulary is possible so that one can build a
simulator with both the soundness and completeness properties. We prove several
negative results: All sound qualitative simulators, employing subsets of the
QSIM representation which retain the operating region transition feature, and
support at least the addition and constancy constraints, are shown to be
inherently incomplete. Even when the simulations are restricted to run in a
single operating region, a constraint vocabulary containing just the addition,
constancy, derivative, and multiplication relations makes the construction of
sound and complete qualitative simulators impossible.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:44:00 GMT"
}
] | 1,321,833,600,000 | [
[
"Yılmaz",
"Ö.",
""
],
[
"Say",
"A. C. C.",
""
]
] |
1110.0023 | L. Liu | L. Liu, M. Truszczynski | Properties and Applications of Programs with Monotone and Convex
Constraints | null | Journal Of Artificial Intelligence Research, Volume 27, pages
299-334, 2006 | 10.1613/jair.2009 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study properties of programs with monotone and convex constraints. We
extend to these formalisms concepts and results from normal logic programming.
They include the notions of strong and uniform equivalence with their
characterizations, tight programs and Fages Lemma, program completion and loop
formulas. Our results provide an abstract account of properties of some recent
extensions of logic programming with aggregates, especially the formalism of
lparse programs. They imply a method to compute stable models of lparse
programs by means of off-the-shelf solvers of pseudo-boolean constraints, which
is often much faster than the smodels system.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:51:03 GMT"
}
] | 1,317,686,400,000 | [
[
"Liu",
"L.",
""
],
[
"Truszczynski",
"M.",
""
]
] |
1110.0024 | S. F. Smith | S. F. Smith, M. J. Streeter | How the Landscape of Random Job Shop Scheduling Instances Depends on the
Ratio of Jobs to Machines | null | Journal Of Artificial Intelligence Research, Volume 26, pages
247-287, 2006 | 10.1613/jair.2013 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We characterize the search landscape of random instances of the job shop
scheduling problem (JSP). Specifically, we investigate how the expected values
of (1) backbone size, (2) distance between near-optimal schedules, and (3)
makespan of random schedules vary as a function of the job to machine ratio
(N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we
provide analytical results, while for intermediate values of N/M we perform
experiments. We prove that as N/M approaches 0, backbone size approaches 100%,
while as N/M approaches infinity the backbone vanishes. In the process we show
that as N/M approaches 0 (resp. N/M approaches infinity), simple priority rules
almost surely generate an optimal schedule, providing theoretical evidence of
an "easy-hard-easy" pattern of typical-case instance difficulty in job shop
scheduling. We also draw connections between our theoretical results and the
"big valley" picture of JSP landscapes.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:51:28 GMT"
}
] | 1,317,686,400,000 | [
[
"Smith",
"S. F.",
""
],
[
"Streeter",
"M. J.",
""
]
] |
1110.0026 | B. Faltings | B. Faltings, P. Pu, P. Viappiani | Preference-based Search using Example-Critiquing with Suggestions | null | Journal Of Artificial Intelligence Research, Volume 27, pages
465-503, 2006 | 10.1613/jair.2075 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider interactive tools that help users search for their most preferred
item in a large collection of options. In particular, we examine
example-critiquing, a technique for enabling users to incrementally construct
preference models by critiquing example options that are presented to them. We
present novel techniques for improving the example-critiquing technology by
adding suggestions to its displayed options. Such suggestions are calculated
based on an analysis of users current preference model and their potential
hidden preferences. We evaluate the performance of our model-based suggestion
techniques with both synthetic and real users. Results show that such
suggestions are highly attractive to users and can stimulate them to express
more preferences to improve the chance of identifying their most preferred item
by up to 78%.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:55:50 GMT"
}
] | 1,317,686,400,000 | [
[
"Faltings",
"B.",
""
],
[
"Pu",
"P.",
""
],
[
"Viappiani",
"P.",
""
]
] |
1110.0027 | Daniel Bryce | J. Pineau, G. Gordon, S. Thrun | Anytime Point-Based Approximations for Large POMDPs | null | Journal Of Artificial Intelligence Research, Volume 27, pages
335-380, 2006 | 10.1613/jair.2078 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Partially Observable Markov Decision Process has long been recognized as
a rich framework for real-world planning and control problems, especially in
robotics. However exact solutions in this framework are typically
computationally intractable for all but the smallest problems. A well-known
technique for speeding up POMDP solving involves performing value backups at
specific belief points, rather than over the entire belief simplex. The
efficiency of this approach, however, depends greatly on the selection of
points. This paper presents a set of novel techniques for selecting informative
belief points which work well in practice. The point selection procedure is
combined with point-based value backups to form an effective anytime POMDP
algorithm called Point-Based Value Iteration (PBVI). The first aim of this
paper is to introduce this algorithm and present a theoretical analysis
justifying the choice of belief selection technique. The second aim of this
paper is to provide a thorough empirical comparison between PBVI and other
state-of-the-art POMDP methods, in particular the Perseus algorithm, in an
effort to highlight their similarities and differences. Evaluation is performed
using both standard POMDP domains and realistic robotic tasks.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:56:49 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Oct 2011 15:08:13 GMT"
}
] | 1,317,772,800,000 | [
[
"Pineau",
"J.",
""
],
[
"Gordon",
"G.",
""
],
[
"Thrun",
"S.",
""
]
] |
1110.0028 | C. Guestrin | C. Guestrin, M. Hauskrecht, B. Kveton | Solving Factored MDPs with Hybrid State and Action Variables | null | Journal Of Artificial Intelligence Research, Volume 27, pages
153-201, 2006 | 10.1613/jair.2085 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions. The central idea of HALP is to approximate the optimal value
function by a linear combination of basis functions and optimize its weights by
linear programming. We analyze both theoretical and computational aspects of
this approach, and demonstrate its scale-up potential on several hybrid
optimization problems.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:57:35 GMT"
}
] | 1,317,686,400,000 | [
[
"Guestrin",
"C.",
""
],
[
"Hauskrecht",
"M.",
""
],
[
"Kveton",
"B.",
""
]
] |
1110.0029 | Xavier Carreras | M. Surdeanu, L. Marquez, X. Carreras, P. R. Comas | Combination Strategies for Semantic Role Labeling | null | Journal Of Artificial Intelligence Research, Volume 29, pages
105-151, 2007 | 10.1613/jair.2088 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context. We evaluate the proposed
inference strategies in the framework of the CoNLL-2005 shared task using only
automatically-generated syntactic information. The extensive experimental
evaluation and analysis indicates that all the proposed inference strategies
are successful -they all outperform the current best results reported in the
CoNLL-2005 evaluation exercise- but each of the proposed approaches has its
advantages and disadvantages. Several important traits of a state-of-the-art
SRL combination strategy emerge from this analysis: (i) individual models
should be combined at the granularity of candidate arguments rather than at the
granularity of complete solutions; (ii) the best combination strategy uses an
inference model based in learning; and (iii) the learning-based inference
benefits from max-margin classifiers and global feedback.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2011 20:58:00 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Oct 2011 17:05:51 GMT"
}
] | 1,426,723,200,000 | [
[
"Surdeanu",
"M.",
""
],
[
"Marquez",
"L.",
""
],
[
"Carreras",
"X.",
""
],
[
"Comas",
"P. R.",
""
]
] |
1110.0248 | Yongzhi Cao | Yongzhi Cao, Huaiqing Wang, Sherry X. Sun, and Guoqing Chen | A Behavioral Distance for Fuzzy-Transition Systems | 12 double column pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | In contrast to the existing approaches to bisimulation for fuzzy systems, we
introduce a behavioral distance to measure the behavioral similarity of states
in a nondeterministic fuzzy-transition system. This behavioral distance is
defined as the greatest fixed point of a suitable monotonic function and
provides a quantitative analogue of bisimilarity. The behavioral distance has
the important property that two states are at zero distance if and only if they
are bisimilar. Moreover, for any given threshold, we find that states with
behavioral distances bounded by the threshold are equivalent. In addition, we
show that two system combinators---parallel composition and product---are
non-expansive with respect to our behavioral distance, which makes
compositional verification possible.
| [
{
"version": "v1",
"created": "Mon, 3 Oct 2011 00:32:22 GMT"
}
] | 1,426,723,200,000 | [
[
"Cao",
"Yongzhi",
""
],
[
"Wang",
"Huaiqing",
""
],
[
"Sun",
"Sherry X.",
""
],
[
"Chen",
"Guoqing",
""
]
] |
1110.1016 | S. Edelkamp | S. Edelkamp, R. Englert, J. Hoffmann, F. Liporace, S. Thiebaux, S.
Trueg | Engineering Benchmarks for Planning: the Domains Used in the
Deterministic Part of IPC-4 | null | Journal Of Artificial Intelligence Research, Volume 26, pages
453-541, 2006 | 10.1613/jair.1982 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a field of research about general reasoning mechanisms, it is essential to
have appropriate benchmarks. Ideally, the benchmarks should reflect possible
applications of the developed technology. In AI Planning, researchers more and
more tend to draw their testing examples from the benchmark collections used in
the International Planning Competition (IPC). In the organization of (the
deterministic part of) the fourth IPC, IPC-4, the authors therefore invested
significant effort to create a useful set of benchmarks. They come from five
different (potential) real-world applications of planning: airport ground
traffic control, oil derivative transportation in pipeline networks,
model-checking safety properties, power supply restoration, and UMTS call
setup. Adapting and preparing such an application for use as a benchmark in the
IPC involves, at the time, inevitable (often drastic) simplifications, as well
as careful choice between, and engineering of, domain encodings. For the first
time in the IPC, we used compilations to formulate complex domain features in
simple languages such as STRIPS, rather than just dropping the more interesting
problem constraints in the simpler language subsets. The article explains and
discusses the five application domains and their adaptation to form the PDDL
test suites used in IPC-4. We summarize known theoretical results on structural
properties of the domains, regarding their computational complexity and
provable properties of their topology under the h+ function (an idealized
version of the relaxed plan heuristic). We present new (empirical) results
illuminating properties such as the quality of the most wide-spread heuristic
functions (planning graph, serial planning graph, and relaxed plan), the growth
of propositional representations over instance size, and the number of actions
available to achieve each fact; we discuss these data in conjunction with the
best results achieved by the different kinds of planners participating in
IPC-4.
| [
{
"version": "v1",
"created": "Thu, 29 Sep 2011 19:02:41 GMT"
}
] | 1,317,859,200,000 | [
[
"Edelkamp",
"S.",
""
],
[
"Englert",
"R.",
""
],
[
"Hoffmann",
"J.",
""
],
[
"Liporace",
"F.",
""
],
[
"Thiebaux",
"S.",
""
],
[
"Trueg",
"S.",
""
]
] |
1110.2200 | M. Fox | M. Fox, D. Long | Modelling Mixed Discrete-Continuous Domains for Planning | null | Journal Of Artificial Intelligence Research, Volume 27, pages
235-297, 2006 | 10.1613/jair.2044 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present pddl+, a planning domain description language for
modelling mixed discrete-continuous planning domains. We describe the syntax
and modelling style of pddl+, showing that the language makes convenient the
modelling of complex time-dependent effects. We provide a formal semantics for
pddl+ by mapping planning instances into constructs of hybrid automata. Using
the syntax of HAs as our semantic model we construct a semantic mapping to
labelled transition systems to complete the formal interpretation of pddl+
planning instances. An advantage of building a mapping from pddl+ to HA theory
is that it forms a bridge between the Planning and Real Time Systems research
communities. One consequence is that we can expect to make use of some of the
theoretical properties of HAs. For example, for a restricted class of HAs the
Reachability problem (which is equivalent to Plan Existence) is decidable.
pddl+ provides an alternative to the continuous durative action model of
pddl2.1, adding a more flexible and robust model of time-dependent behaviour.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 21:16:30 GMT"
}
] | 1,318,377,600,000 | [
[
"Fox",
"M.",
""
],
[
"Long",
"D.",
""
]
] |
1110.2203 | R. H. C. Yap | R. H. C. Yap, Y. Zhang | Set Intersection and Consistency in Constraint Networks | null | Journal Of Artificial Intelligence Research, Volume 27, pages
441-464, 2006 | 10.1613/jair.2058 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we show that there is a close relation between consistency in
a constraint network and set intersection. A proof schema is provided as a
generic way to obtain consistency properties from properties on set
intersection. This approach not only simplifies the understanding of and
unifies many existing consistency results, but also directs the study of
consistency to that of set intersection properties in many situations, as
demonstrated by the results on the convexity and tightness of constraints in
this paper. Specifically, we identify a new class of tree convex constraints
where local consistency ensures global consistency. This generalizes row convex
constraints. Various consistency results are also obtained on constraint
networks where only some, in contrast to all in the existing work,constraints
are tight.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 21:27:27 GMT"
}
] | 1,318,377,600,000 | [
[
"Yap",
"R. H. C.",
""
],
[
"Zhang",
"Y.",
""
]
] |
1110.2204 | J. Culberson | J. Culberson, Y. Gao | Consistency and Random Constraint Satisfaction Models | null | Journal Of Artificial Intelligence Research, Volume 28, pages
517-557, 2007 | 10.1613/jair.2155 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the possibility of designing non-trivial random CSP
models by exploiting the intrinsic connection between structures and
typical-case hardness. We show that constraint consistency, a notion that has
been developed to improve the efficiency of CSP algorithms, is in fact the key
to the design of random CSP models that have interesting phase transition
behavior and guaranteed exponential resolution complexity without putting much
restriction on the parameter of constraint tightness or the domain size of the
problem. We propose a very flexible framework for constructing problem
instances withinteresting behavior and develop a variety of concrete methods to
construct specific random CSP models that enforce different levels of
constraint consistency. A series of experimental studies with interesting
observations are carried out to illustrate the effectiveness of introducing
structural elements in random instances, to verify the robustness of our
proposal, and to investigate features of some specific models based on our
framework that are highly related to the behavior of backtracking search
algorithms.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 21:35:30 GMT"
}
] | 1,318,377,600,000 | [
[
"Culberson",
"J.",
""
],
[
"Gao",
"Y.",
""
]
] |
1110.2205 | E. Pontelli | E. Pontelli, T. C. Son, P. H. Tu | Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms | null | Journal Of Artificial Intelligence Research, Volume 29, pages
353-389, 2007 | 10.1613/jair.2171 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present two alternative approaches to defining answer sets
for logic programs with arbitrary types of abstract constraint atoms (c-atoms).
These approaches generalize the fixpoint-based and the level mapping based
answer set semantics of normal logic programs to the case of logic programs
with arbitrary types of c-atoms. The results are four different answer set
definitions which are equivalent when applied to normal logic programs. The
standard fixpoint-based semantics of logic programs is generalized in two
directions, called answer set by reduct and answer set by complement. These
definitions, which differ from each other in the treatment of
negation-as-failure (naf) atoms, make use of an immediate consequence operator
to perform answer set checking, whose definition relies on the notion of
conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other
two definitions, called strongly and weakly well-supported models, are
generalizations of the notion of well-supported models of normal logic programs
to the case of programs with c-atoms. As for the case of fixpoint-based
semantics, the difference between these two definitions is rooted in the
treatment of naf atoms. We prove that answer sets by reduct (resp. by
complement) are equivalent to weakly (resp. strongly) well-supported models of
a program, thus generalizing the theorem on the correspondence between stable
models and well-supported models of a normal logic program to the class of
programs with c-atoms. We show that the newly defined semantics coincide with
previously introduced semantics for logic programs with monotone c-atoms, and
they extend the original answer set semantics of normal logic programs. We also
study some properties of answer sets of programs with c-atoms, and relate our
definitions to several semantics for logic programs with aggregates presented
in the literature.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 21:36:17 GMT"
}
] | 1,318,377,600,000 | [
[
"Pontelli",
"E.",
""
],
[
"Son",
"T. C.",
""
],
[
"Tu",
"P. H.",
""
]
] |
1110.2209 | A. S. Fukunaga | A. S. Fukunaga, R. E. Korf | Bin Completion Algorithms for Multicontainer Packing, Knapsack, and
Covering Problems | null | Journal Of Artificial Intelligence Research, Volume 28, pages
393-429, 2007 | 10.1613/jair.2106 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many combinatorial optimization problems such as the bin packing and multiple
knapsack problems involve assigning a set of discrete objects to multiple
containers. These problems can be used to model task and resource allocation
problems in multi-agent systems and distributed systms, and can also be found
as subproblems of scheduling problems. We propose bin completion, a
branch-and-bound strategy for one-dimensional, multicontainer packing problems.
Bin completion combines a bin-oriented search space with a powerful dominance
criterion that enables us to prune much of the space. The performance of the
basic bin completion framework can be enhanced by using a number of extensions,
including nogood-based pruning techniques that allow further exploitation of
the dominance criterion. Bin completion is applied to four problems: multiple
knapsack, bin covering, min-cost covering, and bin packing. We show that our
bin completion algorithms yield new, state-of-the-art results for the multiple
knapsack, bin covering, and min-cost covering problems, outperforming previous
algorithms by several orders of magnitude with respect to runtime on some
classes of hard, random problem instances. For the bin packing problem, we
demonstrate significant improvements compared to most previous results, but
show that bin completion is not competitive with current state-of-the-art
cutting-stock based approaches.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 21:55:37 GMT"
}
] | 1,318,377,600,000 | [
[
"Fukunaga",
"A. S.",
""
],
[
"Korf",
"R. E.",
""
]
] |
1110.2212 | F. Rossi | F. Rossi, K. B. Venable, N. Yorke-Smith | Uncertainty in Soft Temporal Constraint Problems:A General Framework and
Controllability Algorithms for the Fuzzy Case | null | Journal Of Artificial Intelligence Research, Volume 27, pages
617-674, 2006 | 10.1613/jair.2135 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In real-life temporal scenarios, uncertainty and preferences are often
essential and coexisting aspects. We present a formalism where quantitative
temporal constraints with both preferences and uncertainty can be defined. We
show how three classical notions of controllability (that is, strong, weak, and
dynamic), which have been developed for uncertain temporal problems, can be
generalized to handle preferences as well. After defining this general
framework, we focus on problems where preferences follow the fuzzy approach,
and with properties that assure tractability. For such problems, we propose
algorithms to check the presence of the controllability properties. In
particular, we show that in such a setting dealing simultaneously with
preferences and uncertainty does not increase the complexity of controllability
testing. We also develop a dynamic execution algorithm, of polynomial
complexity, that produces temporal plans under uncertainty that are optimal
with respect to fuzzy preferences.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 22:02:16 GMT"
}
] | 1,618,185,600,000 | [
[
"Rossi",
"F.",
""
],
[
"Venable",
"K. B.",
""
],
[
"Yorke-Smith",
"N.",
""
]
] |
1110.2213 | C. Bettini | C. Bettini, S. Mascetti, X. S. Wang | Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal
Periodic Sets | null | Journal Of Artificial Intelligence Research, Volume 28, pages
299-348, 2007 | 10.1613/jair.2136 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the recent years several research efforts have focused on the concept of
time granularity and its applications. A first stream of research investigated
the mathematical models behind the notion of granularity and the algorithms to
manage temporal data based on those models. A second stream of research
investigated symbolic formalisms providing a set of algebraic operators to
define granularities in a compact and compositional way. However, only very
limited manipulation algorithms have been proposed to operate directly on the
algebraic representation making it unsuitable to use the symbolic formalisms in
applications that need manipulation of granularities.
This paper aims at filling the gap between the results from these two streams
of research, by providing an efficient conversion from the algebraic
representation to the equivalent low-level representation based on the
mathematical models. In addition, the conversion returns a minimal
representation in terms of period length. Our results have a major practical
impact: users can more easily define arbitrary granularities in terms of
algebraic operators, and then access granularity reasoning and other services
operating efficiently on the equivalent, minimal low-level representation. As
an example, we illustrate the application to temporal constraint reasoning with
multiple granularities.
From a technical point of view, we propose an hybrid algorithm that
interleaves the conversion of calendar subexpressions into periodical sets with
the minimization of the period length. The algorithm returns set-based
granularity representations having minimal period length, which is the most
relevant parameter for the performance of the considered reasoning services.
Extensive experimental work supports the techniques used in the algorithm, and
shows the efficiency and effectiveness of the algorithm.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 22:03:55 GMT"
}
] | 1,318,377,600,000 | [
[
"Bettini",
"C.",
""
],
[
"Mascetti",
"S.",
""
],
[
"Wang",
"X. S.",
""
]
] |
1110.2216 | P. F. Felzenszwalb | P. F. Felzenszwalb, D. McAllester | The Generalized A* Architecture | null | Journal Of Artificial Intelligence Research, Volume 29, pages
153-190, 2007 | 10.1613/jair.2187 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of computing a lightest derivation of a global
structure using a set of weighted rules. A large variety of inference problems
in AI can be formulated in this framework. We generalize A* search and
heuristics derived from abstractions to a broad class of lightest derivation
problems. We also describe a new algorithm that searches for lightest
derivations using a hierarchy of abstractions. Our generalization of A* gives a
new algorithm for searching AND/OR graphs in a bottom-up fashion. We discuss
how the algorithms described here provide a general architecture for addressing
the pipeline problem --- the problem of passing information back and forth
between various stages of processing in a perceptual system. We consider
examples in computer vision and natural language processing. We apply the
hierarchical search algorithm to the problem of estimating the boundaries of
convex objects in grayscale images and compare it to other search methods. A
second set of experiments demonstrate the use of a new compositional model for
finding salient curves in images.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2011 22:14:15 GMT"
}
] | 1,318,377,600,000 | [
[
"Felzenszwalb",
"P. F.",
""
],
[
"McAllester",
"D.",
""
]
] |
1110.2726 | D. Gabelaia | D. Gabelaia, R. Kontchakov, A. Kurucz, F. Wolter, M. Zakharyaschev | Combining Spatial and Temporal Logics: Expressiveness vs. Complexity | null | Journal Of Artificial Intelligence Research, Volume 23, pages
167-243, 2005 | 10.1613/jair.1537 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we construct and investigate a hierarchy of spatio-temporal
formalisms that result from various combinations of propositional spatial and
temporal logics such as the propositional temporal logic PTL, the spatial
logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a
clear picture of the trade-off between expressiveness and computational
realisability within the hierarchy. We demonstrate how different combining
principles as well as spatial and temporal primitives can produce NP-, PSPACE-,
EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out
of components that are at most NP- or PSPACE-complete.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:17:41 GMT"
}
] | 1,318,464,000,000 | [
[
"Gabelaia",
"D.",
""
],
[
"Kontchakov",
"R.",
""
],
[
"Kurucz",
"A.",
""
],
[
"Wolter",
"F.",
""
],
[
"Zakharyaschev",
"M.",
""
]
] |
1110.2728 | A. Gerevini | A. Gerevini, A. Saetti, I. Serina | An Approach to Temporal Planning and Scheduling in Domains with
Predictable Exogenous Events | null | Journal Of Artificial Intelligence Research, Volume 25, pages
187-231, 2006 | 10.1613/jair.1742 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The treatment of exogenous events in planning is practically important in
many real-world domains where the preconditions of certain plan actions are
affected by such events. In this paper we focus on planning in temporal domains
with exogenous events that happen at known times, imposing the constraint that
certain actions in the plan must be executed during some predefined time
windows. When actions have durations, handling such temporal constraints adds
an extra difficulty to planning. We propose an approach to planning in these
domains which integrates constraint-based temporal reasoning into a graph-based
planning framework using local search. Our techniques are implemented in a
planner that took part in the 4th International Planning Competition (IPC-4). A
statistical analysis of the results of IPC-4 demonstrates the effectiveness of
our approach in terms of both CPU-time and plan quality. Additional experiments
show the good performance of the temporal reasoning techniques integrated into
our planner.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:18:04 GMT"
}
] | 1,318,464,000,000 | [
[
"Gerevini",
"A.",
""
],
[
"Saetti",
"A.",
""
],
[
"Serina",
"I.",
""
]
] |
1110.2729 | F. Bacchus | F. Bacchus | The Power of Modeling - a Response to PDDL2.1 | null | Journal Of Artificial Intelligence Research, Volume 20, pages
125-132, 2003 | 10.1613/jair.1993 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this commentary I argue that although PDDL is a very useful standard for
the planning competition, its design does not properly consider the issue of
domain modeling. Hence, I would not advocate its use in specifying planning
domains outside of the context of the planning competition. Rather, the field
needs to explore different approaches and grapple more directly with the
problem of effectively modeling and utilizing all of the diverse pieces of
knowledge we typically have about planning domains.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:18:48 GMT"
}
] | 1,318,464,000,000 | [
[
"Bacchus",
"F.",
""
]
] |
1110.2730 | M. S. Boddy | M. S. Boddy | Imperfect Match: PDDL 2.1 and Real Applications | null | Journal Of Artificial Intelligence Research, Volume 20, pages
133-137, 2003 | 10.1613/jair.1994 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | PDDL was originally conceived and constructed as a lingua franca for the
International Planning Competition. PDDL2.1 embodies a set of extensions
intended to support the expression of something closer to real planning
problems. This objective has only been partially achieved, due in large part to
a deliberate focus on not moving too far from classical planning models and
solution methods.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:19:10 GMT"
}
] | 1,318,464,000,000 | [
[
"Boddy",
"M. S.",
""
]
] |
1110.2731 | H. A. Geffner | H. A. Geffner | PDDL 2.1: Representation vs. Computation | null | Journal Of Artificial Intelligence Research, Volume 20, pages
139-144, 2003 | 10.1613/jair.1995 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I comment on the PDDL 2.1 language and its use in the planning competition,
focusing on the choices made for accommodating time and concurrency. I also
discuss some methodological issues that have to do with the move toward more
expressive planning languages and the balance needed in planning research
between semantics and computation.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:19:24 GMT"
}
] | 1,318,464,000,000 | [
[
"Geffner",
"H. A.",
""
]
] |
1110.2732 | J. C. Beck | J. C. Beck, N. Wilson | Proactive Algorithms for Job Shop Scheduling with Probabilistic
Durations | null | Journal Of Artificial Intelligence Research, Volume 28, pages
183-232, 2007 | 10.1613/jair.2080 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most classical scheduling formulations assume a fixed and known duration for
each activity. In this paper, we weaken this assumption, requiring instead that
each duration can be represented by an independent random variable with a known
mean and variance. The best solutions are ones which have a high probability of
achieving a good makespan. We first create a theoretical framework, formally
showing how Monte Carlo simulation can be combined with deterministic
scheduling algorithms to solve this problem. We propose an associated
deterministic scheduling problem whose solution is proved, under certain
conditions, to be a lower bound for the probabilistic problem. We then propose
and investigate a number of techniques for solving such problems based on
combinations of Monte Carlo simulation, solutions to the associated
deterministic problem, and either constraint programming or tabu search. Our
empirical results demonstrate that a combination of the use of the associated
deterministic problem and Monte Carlo simulation results in algorithms that
scale best both in terms of problem size and uncertainty. Further experiments
point to the correlation between the quality of the deterministic solution and
the quality of the probabilistic solution as a major factor responsible for
this success.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:19:43 GMT"
}
] | 1,318,464,000,000 | [
[
"Beck",
"J. C.",
""
],
[
"Wilson",
"N.",
""
]
] |
1110.2734 | A. Darwiche | A. Darwiche, J. Huang | The Language of Search | null | Journal Of Artificial Intelligence Research, Volume 29, pages
191-219, 2007 | 10.1613/jair.2097 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with a class of algorithms that perform exhaustive
search on propositional knowledge bases. We show that each of these algorithms
defines and generates a propositional language. Specifically, we show that the
trace of a search can be interpreted as a combinational circuit, and a search
algorithm then defines a propositional language consisting of circuits that are
generated across all possible executions of the algorithm. In particular, we
show that several versions of exhaustive DPLL search correspond to such
well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF.
By thus mapping search algorithms to propositional languages, we provide a
uniform and practical framework in which successful search techniques can be
harnessed for compilation of knowledge into various languages of interest, and
a new methodology whereby the power and limitations of search algorithms can be
understood by looking up the tractability and succinctness of the corresponding
propositional languages.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:20:23 GMT"
}
] | 1,318,464,000,000 | [
[
"Darwiche",
"A.",
""
],
[
"Huang",
"J.",
""
]
] |
1110.2735 | L. Barbulescu | L. Barbulescu, A. E. Howe, M. Roberts, L. D. Whitley | Understanding Algorithm Performance on an Oversubscribed Scheduling
Application | null | Journal Of Artificial Intelligence Research, Volume 27, pages
577-615, 2006 | 10.1613/jair.2038 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The best performing algorithms for a particular oversubscribed scheduling
application, Air Force Satellite Control Network (AFSCN) scheduling, appear to
have little in common. Yet, through careful experimentation and modeling of
performance in real problem instances, we can relate characteristics of the
best algorithms to characteristics of the application. In particular, we find
that plateaus dominate the search spaces (thus favoring algorithms that make
larger changes to solutions) and that some randomization in exploration is
critical to good performance (due to the lack of gradient information on the
plateaus). Based on our explanations of algorithm performance, we develop a new
algorithm that combines characteristics of the best performers; the new
algorithms performance is better than the previous best. We show how hypothesis
driven experimentation and search modeling can both explain algorithm
performance and motivate the design of a new algorithm.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:21:07 GMT"
}
] | 1,318,464,000,000 | [
[
"Barbulescu",
"L.",
""
],
[
"Howe",
"A. E.",
""
],
[
"Roberts",
"M.",
""
],
[
"Whitley",
"L. D.",
""
]
] |
1110.2736 | A. I. Coles | A. I. Coles, A. J. Smith | Marvin: A Heuristic Search Planner with Online Macro-Action Learning | null | Journal Of Artificial Intelligence Research, Volume 28, pages
119-156, 2007 | 10.1613/jair.2077 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes Marvin, a planner that competed in the Fourth
International Planning Competition (IPC 4). Marvin uses
action-sequence-memoisation techniques to generate macro-actions, which are
then used during search for a solution plan. We provide an overview of its
architecture and search behaviour, detailing the algorithms used. We also
empirically demonstrate the effectiveness of its features in various planning
domains; in particular, the effects on performance due to the use of
macro-actions, the novel features of its search behaviour, and the native
support of ADL and Derived Predicates.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:21:41 GMT"
}
] | 1,318,464,000,000 | [
[
"Coles",
"A. I.",
""
],
[
"Smith",
"A. J.",
""
]
] |
1110.2737 | E. A. Hansen | E. A. Hansen, R. Zhou | Anytime Heuristic Search | null | Journal Of Artificial Intelligence Research, Volume 28, pages
267-297, 2007 | 10.1613/jair.2096 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe how to convert the heuristic search algorithm A* into an anytime
algorithm that finds a sequence of improved solutions and eventually converges
to an optimal solution. The approach we adopt uses weighted heuristic search to
find an approximate solution quickly, and then continues the weighted search to
find improved solutions as well as to improve a bound on the suboptimality of
the current solution. When the time available to solve a search problem is
limited or uncertain, this creates an anytime heuristic search algorithm that
allows a flexible tradeoff between search time and solution quality. We analyze
the properties of the resulting Anytime A* algorithm, and consider its
performance in three domains; sliding-tile puzzles, STRIPS planning, and
multiple sequence alignment. To illustrate the generality of this approach, we
also describe how to transform the memory-efficient search algorithm Recursive
Best-First Search (RBFS) into an anytime algorithm.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:21:59 GMT"
}
] | 1,318,464,000,000 | [
[
"Hansen",
"E. A.",
""
],
[
"Zhou",
"R.",
""
]
] |
1110.2738 | Y. Chen | Y. Chen, F. Lin | Discovering Classes of Strongly Equivalent Logic Programs | null | Journal Of Artificial Intelligence Research, Volume 28, pages
431-451, 2007 | 10.1613/jair.2131 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we apply computer-aided theorem discovery technique to discover
theorems about strongly equivalent logic programs under the answer set
semantics. Our discovered theorems capture new classes of strongly equivalent
logic programs that can lead to new program simplification rules that preserve
strong equivalence. Specifically, with the help of computers, we discovered
exact conditions that capture the strong equivalence between a rule and the
empty set, between two rules, between two rules and one of the two rules,
between two rules and another rule, and between three rules and two of the
three rules.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:22:15 GMT"
}
] | 1,318,464,000,000 | [
[
"Chen",
"Y.",
""
],
[
"Lin",
"F.",
""
]
] |
1110.2739 | N. Creignou | N. Creignou, H. Daude, U. Egly | Phase Transition for Random Quantified XOR-Formulas | null | Journal Of Artificial Intelligence Research, Volume 29, pages
1-18, 2007 | 10.1613/jair.2120 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The QXORSAT problem is the quantified version of the satisfiability problem
XORSAT in which the connective exclusive-or is used instead of the usual or. We
study the phase transition associated with random QXORSAT instances. We give a
description of this phase transition in the case of one alternation of
quantifiers, thus performing an advanced practical and theoretical study on the
phase transition of a quantified roblem.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:22:52 GMT"
}
] | 1,318,464,000,000 | [
[
"Creignou",
"N.",
""
],
[
"Daude",
"H.",
""
],
[
"Egly",
"U.",
""
]
] |
1110.2740 | B. Bidyuk | B. Bidyuk, R. Dechter | Cutset Sampling for Bayesian Networks | null | Journal Of Artificial Intelligence Research, Volume 28, pages
1-48, 2007 | 10.1613/jair.2149 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents a new sampling methodology for Bayesian networks that
samples only a subset of variables and applies exact inference to the rest.
Cutset sampling is a network structure-exploiting application of the
Rao-Blackwellisation principle to sampling in Bayesian networks. It improves
convergence by exploiting memory-based inference algorithms. It can also be
viewed as an anytime approximation of the exact cutset-conditioning algorithm
developed by Pearl. Cutset sampling can be implemented efficiently when the
sampled variables constitute a loop-cutset of the Bayesian network and, more
generally, when the induced width of the networks graph conditioned on the
observed sampled variables is bounded by a constant w. We demonstrate
empirically the benefit of this scheme on a range of benchmarks.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:23:15 GMT"
}
] | 1,318,464,000,000 | [
[
"Bidyuk",
"B.",
""
],
[
"Dechter",
"R.",
""
]
] |
1110.2741 | C. Pralet | C. Pralet, T. Schiex, G. Verfaillie | An Algebraic Graphical Model for Decision with Uncertainties,
Feasibilities, and Utilities | null | Journal Of Artificial Intelligence Research, Volume 29, pages
421-489, 2007 | 10.1613/jair.2151 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous formalisms and dedicated algorithms have been designed in the last
decades to model and solve decision making problems. Some formalisms, such as
constraint networks, can express "simple" decision problems, while others are
designed to take into account uncertainties, unfeasible decisions, and
utilities. Even in a single formalism, several variants are often proposed to
model different types of uncertainty (probability, possibility...) or utility
(additive or not). In this article, we introduce an algebraic graphical model
that encompasses a large number of such formalisms: (1) we first adapt previous
structures from Friedman, Chu and Halpern for representing uncertainty,
utility, and expected utility in order to deal with generic forms of sequential
decision making; (2) on these structures, we then introduce composite graphical
models that express information via variables linked by "local" functions,
thanks to conditional independence; (3) on these graphical models, we finally
define a simple class of queries which can represent various scenarios in terms
of observabilities and controllabilities. A natural decision-tree semantics for
such queries is completed by an equivalent operational semantics, which induces
generic algorithms. The proposed framework, called the
Plausibility-Feasibility-Utility (PFU) framework, not only provides a better
understanding of the links between existing formalisms, but it also covers yet
unpublished frameworks (such as possibilistic influence diagrams) and unifies
formalisms such as quantified boolean formulas and influence diagrams. Our
backtrack and variable elimination generic algorithms are a first step towards
unified algorithms.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:23:33 GMT"
}
] | 1,318,464,000,000 | [
[
"Pralet",
"C.",
""
],
[
"Schiex",
"T.",
""
],
[
"Verfaillie",
"G.",
""
]
] |
1110.2742 | T. Di Noia | T. Di Noia, E. Di Sciascio, F. M. Donini | Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic
Approach | null | Journal Of Artificial Intelligence Research, Volume 29, pages
269-307, 2007 | 10.1613/jair.2153 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matchmaking arises when supply and demand meet in an electronic marketplace,
or when agents search for a web service to perform some task, or even when
recruiting agencies match curricula and job profiles. In such open
environments, the objective of a matchmaking process is to discover best
available offers to a given request. We address the problem of matchmaking from
a knowledge representation perspective, with a formalization based on
Description Logics. We devise Concept Abduction and Concept Contraction as
non-monotonic inferences in Description Logics suitable for modeling
matchmaking in a logical framework, and prove some related complexity results.
We also present reasonable algorithms for semantic matchmaking based on the
devised inferences, and prove that they obey to some commonsense properties.
Finally, we report on the implementation of the proposed matchmaking framework,
which has been used both as a mediator in e-marketplaces and for semantic web
services discovery.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:23:49 GMT"
}
] | 1,318,464,000,000 | [
[
"Di Noia",
"T.",
""
],
[
"Di Sciascio",
"E.",
""
],
[
"Donini",
"F. M.",
""
]
] |
1110.2743 | J. C. Beck | J. C. Beck | Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling | null | Journal Of Artificial Intelligence Research, Volume 29, pages
49-77, 2007 | 10.1613/jair.2169 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel
constructive search technique that performs a series of resource-limited tree
searches where each search begins either from an empty solution (as in
randomized restart) or from a solution that has been encountered during the
search. A small number of these "elite solutions is maintained during the
search. We introduce the technique and perform three sets of experiments on the
job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS
is carried out to evaluate the performance impact of various parameter
settings. Second, we inquire into the diversity of the elite solution set,
showing, contrary to expectations, that a less diverse set leads to stronger
performance. Finally, we compare the best parameter setting of SGMPCS from the
first two experiments to chronological backtracking, limited discrepancy
search, randomized restart, and a sophisticated tabu search algorithm on a set
of well-known benchmark problems. Results demonstrate that SGMPCS is
significantly better than the other constructive techniques tested, though lags
behind the tabu search.
| [
{
"version": "v1",
"created": "Wed, 12 Oct 2011 18:24:37 GMT"
}
] | 1,318,464,000,000 | [
[
"Beck",
"J. C.",
""
]
] |
1110.3002 | Carlos Gershenson | Carlos Gershenson | Are Minds Computable? | 7 pages, comments welcome | null | null | C3 Report 2011.08 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This essay explores the limits of Turing machines concerning the modeling of
minds and suggests alternatives to go beyond those limits.
| [
{
"version": "v1",
"created": "Thu, 13 Oct 2011 17:26:03 GMT"
}
] | 1,318,550,400,000 | [
[
"Gershenson",
"Carlos",
""
]
] |
1110.3385 | Tshilidzi Marwala | Pretesh Patel and Tshilidzi Marwala | Fuzzy Inference Systems Optimization | Paper Submitted to INTECH | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper compares various optimization methods for fuzzy inference system
optimization. The optimization methods compared are genetic algorithm, particle
swarm optimization and simulated annealing. When these techniques were
implemented it was observed that the performance of each technique within the
fuzzy inference system classification was context dependent.
| [
{
"version": "v1",
"created": "Sat, 15 Oct 2011 05:39:34 GMT"
}
] | 1,318,896,000,000 | [
[
"Patel",
"Pretesh",
""
],
[
"Marwala",
"Tshilidzi",
""
]
] |
1110.3888 | Yuming Xu | Xu Yuming | Handling controversial arguments by matrix | 21 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce matrix and its block to the Dung's theory of argumentation
framework. It is showed that each argumentation framework has a matrix
representation, and the indirect attack relation and indirect defence relation
can be characterized by computing the matrix. This provide a powerful
mathematics way to determine the "controversial arguments" in an argumentation
framework. Also, we introduce several kinds of blocks based on the matrix, and
various prudent semantics of argumentation frameworks can all be determined by
computing and comparing the matrices and their blocks which we have defined. In
contrast with traditional method of directed graph, the matrix method has an
excellent advantage: computability(even can be realized on computer easily).
So, there is an intensive perspective to import the theory of matrix to the
research of argumentation frameworks and its related areas.
| [
{
"version": "v1",
"created": "Tue, 18 Oct 2011 07:01:28 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Oct 2011 05:58:23 GMT"
}
] | 1,319,155,200,000 | [
[
"Yuming",
"Xu",
""
]
] |
1110.4076 | V. Bulitko | V. Bulitko, G. Lee | Learning in Real-Time Search: A Unifying Framework | null | Journal Of Artificial Intelligence Research, Volume 25, pages
119-157, 2006 | 10.1613/jair.1789 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 17:00:02 GMT"
}
] | 1,318,982,400,000 | [
[
"Bulitko",
"V.",
""
],
[
"Lee",
"G.",
""
]
] |
1110.4719 | Thierry Petit | Thierry Petit, Nicolas Beldiceanu and Xavier Lorca | A Generalized Arc-Consistency Algorithm for a Class of Counting
Constraints: Revised Edition that Incorporates One Correction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the SEQ BIN meta-constraint with a polytime algorithm
achieving general- ized arc-consistency according to some properties. SEQ BIN
can be used for encoding counting con- straints such as CHANGE, SMOOTH or
INCREAS- ING NVALUE. For some of these constraints and some of their variants
GAC can be enforced with a time and space complexity linear in the sum of
domain sizes, which improves or equals the best known results of the
literature.
| [
{
"version": "v1",
"created": "Fri, 21 Oct 2011 07:49:48 GMT"
}
] | 1,319,414,400,000 | [
[
"Petit",
"Thierry",
""
],
[
"Beldiceanu",
"Nicolas",
""
],
[
"Lorca",
"Xavier",
""
]
] |
1110.5172 | Valmi Dufour-Lussier | Valmi Dufour-Lussier (INRIA Lorraine - LORIA), Florence Le Ber (INRIA
Lorraine - LORIA, LHyGeS), Jean Lieber (INRIA Lorraine - LORIA) | Quels formalismes temporels pour repr\'esenter des connaissances
extraites de textes de recettes de cuisine ? | Repr\'esentation et raisonnement sur le temps et l'espace (2011) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Taaable projet goal is to create a case-based reasoning system for
retrieval and adaptation of cooking recipes. Within this framework, we are
discussing the temporal aspects of recipes and the means of representing those
in order to adapt their text.
| [
{
"version": "v1",
"created": "Mon, 24 Oct 2011 09:33:31 GMT"
}
] | 1,319,500,800,000 | [
[
"Dufour-Lussier",
"Valmi",
"",
"INRIA Lorraine - LORIA"
],
[
"Ber",
"Florence Le",
"",
"INRIA\n Lorraine - LORIA, LHyGeS"
],
[
"Lieber",
"Jean",
"",
"INRIA Lorraine - LORIA"
]
] |
1110.6290 | Lars Kotthoff | Ian P. Gent and Chris Jefferson and Lars Kotthoff and Ian Miguel | Modelling Constraint Solver Architecture Design as a Constraint Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing component-based constraint solvers is a complex problem. Some
components are required, some are optional and there are interdependencies
between the components. Because of this, previous approaches to solver design
and modification have been ad-hoc and limited. We present a system that
transforms a description of the components and the characteristics of the
target constraint solver into a constraint problem. Solving this problem yields
the description of a valid solver. Our approach represents a significant step
towards the automated design and synthesis of constraint solvers that are
specialised for individual constraint problem classes or instances.
| [
{
"version": "v1",
"created": "Fri, 28 Oct 2011 10:41:43 GMT"
}
] | 1,320,019,200,000 | [
[
"Gent",
"Ian P.",
""
],
[
"Jefferson",
"Chris",
""
],
[
"Kotthoff",
"Lars",
""
],
[
"Miguel",
"Ian",
""
]
] |
1110.6589 | Amit Mishra | Amit K. Mishra and Chris Baker | A cognitive diversity framework for radar target classification | null | The IET COGnitive systems with Interactive Sensors 2010 | null | The IET COGnitive systems with Interactive Sensors 2010 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classification of targets by radar has proved to be notoriously difficult
with the best systems still yet to attain sufficiently high levels of
performance and reliability. In the current contribution we explore a new
design of radar based target recognition, where angular diversity is used in a
cognitive manner to attain better performance. Performance is bench- marked
against conventional classification schemes. The proposed scheme can easily be
extended to cognitive target recognition based on multiple diversity
strategies.
| [
{
"version": "v1",
"created": "Sun, 30 Oct 2011 09:26:34 GMT"
}
] | 1,320,105,600,000 | [
[
"Mishra",
"Amit K.",
""
],
[
"Baker",
"Chris",
""
]
] |
1111.0039 | I. Horrocks | I. Horrocks, J. Z. Pan, G. Stamou, G. Stoilos, V. Tzouvaras | Reasoning with Very Expressive Fuzzy Description Logics | null | Journal Of Artificial Intelligence Research, Volume 30, pages
273-320, 2007 | 10.1613/jair.2279 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is widely recognized today that the management of imprecision and
vagueness will yield more intelligent and realistic knowledge-based
applications. Description Logics (DLs) are a family of knowledge representation
languages that have gained considerable attention the last decade, mainly due
to their decidability and the existence of empirically high performance of
reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to
the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms
(S), inverse roles (I), role hierarchies (H) and number restrictions (N). We
illustrate why transitive role axioms are difficult to handle in the presence
of fuzzy interpretations and how to handle them properly. Then we extend these
results by adding role hierarchies and finally number restrictions. The main
contributions of the paper are the decidability proof of the fuzzy DL languages
fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base
satisfiability problem of the fuzzy-SI and fuzzy-SHIN.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 21:37:41 GMT"
}
] | 1,320,192,000,000 | [
[
"Horrocks",
"I.",
""
],
[
"Pan",
"J. Z.",
""
],
[
"Stamou",
"G.",
""
],
[
"Stoilos",
"G.",
""
],
[
"Tzouvaras",
"V.",
""
]
] |
1111.0040 | C. M. Li | C. M. Li, F. Manya, J. Planes | New Inference Rules for Max-SAT | null | Journal Of Artificial Intelligence Research, Volume 30, pages
321-359, 2007 | 10.1613/jair.2215 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exact Max-SAT solvers, compared with SAT solvers, apply little inference at
each node of the proof tree. Commonly used SAT inference rules like unit
propagation produce a simplified formula that preserves satisfiability but,
unfortunately, solving the Max-SAT problem for the simplified formula is not
equivalent to solving it for the original formula. In this paper, we define a
number of original inference rules that, besides being applied efficiently,
transform Max-SAT instances into equivalent Max-SAT instances which are easier
to solve. The soundness of the rules, that can be seen as refinements of unit
resolution adapted to Max-SAT, are proved in a novel and simple way via an
integer programming transformation. With the aim of finding out how powerful
the inference rules are in practice, we have developed a new Max-SAT solver,
called MaxSatz, which incorporates those rules, and performed an experimental
investigation. The results provide empirical evidence that MaxSatz is very
competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph
3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation
2006.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 21:39:39 GMT"
}
] | 1,320,192,000,000 | [
[
"Li",
"C. M.",
""
],
[
"Manya",
"F.",
""
],
[
"Planes",
"J.",
""
]
] |
1111.0043 | B. Faltings | B. Faltings, R. Jurca | Obtaining Reliable Feedback for Sanctioning Reputation Mechanisms | null | Journal Of Artificial Intelligence Research, Volume 29, pages
391-419, 2007 | 10.1613/jair.2243 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reputation mechanisms offer an effective alternative to verification
authorities for building trust in electronic markets with moral hazard. Future
clients guide their business decisions by considering the feedback from past
transactions; if truthfully exposed, cheating behavior is sanctioned and thus
becomes irrational.
It therefore becomes important to ensure that rational clients have the right
incentives to report honestly. As an alternative to side-payment schemes that
explicitly reward truthful reports, we show that honesty can emerge as a
rational behavior when clients have a repeated presence in the market. To this
end we describe a mechanism that supports an equilibrium where truthful
feedback is obtained. Then we characterize the set of pareto-optimal equilibria
of the mechanism, and derive an upper bound on the percentage of false reports
that can be recorded by the mechanism. An important role in the existence of
this bound is played by the fact that rational clients can establish a
reputation for reporting honestly.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 21:43:18 GMT"
}
] | 1,320,192,000,000 | [
[
"Faltings",
"B.",
""
],
[
"Jurca",
"R.",
""
]
] |
1111.0044 | C. Domshlak | C. Domshlak, J. Hoffmann | Probabilistic Planning via Heuristic Forward Search and Weighted Model
Counting | null | Journal Of Artificial Intelligence Research, Volume 30, pages
565-620, 2007 | 10.1613/jair.2289 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 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.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 21:47:05 GMT"
}
] | 1,320,192,000,000 | [
[
"Domshlak",
"C.",
""
],
[
"Hoffmann",
"J.",
""
]
] |
1111.0049 | B. Glimm | Birte Glimm, Ian Horrocks, Carsten Lutz, Ulrike Sattler | Conjunctive Query Answering for the Description Logic SHIQ | null | Journal Of Artificial Intelligence Research, Volume 31, pages
157-204, 2008 | 10.1613/jair.2372 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conjunctive queries play an important role as an expressive query language
for Description Logics (DLs). Although modern DLs usually provide for
transitive roles, conjunctive query answering over DL knowledge bases is only
poorly understood if transitive roles are admitted in the query. In this paper,
we consider unions of conjunctive queries over knowledge bases formulated in
the prominent DL SHIQ and allow transitive roles in both the query and the
knowledge base. We show decidability of query answering in this setting and
establish two tight complexity bounds: regarding combined complexity, we prove
that there is a deterministic algorithm for query answering that needs time
single exponential in the size of the KB and double exponential in the size of
the query, which is optimal. Regarding data complexity, we prove containment in
co-NP.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:01:42 GMT"
}
] | 1,320,192,000,000 | [
[
"Glimm",
"Birte",
""
],
[
"Horrocks",
"Ian",
""
],
[
"Lutz",
"Carsten",
""
],
[
"Sattler",
"Ulrike",
""
]
] |
1111.0051 | George M. Coghill | George M. Coghill, Ross D. King, Ashwin Srinivasan | Qualitative System Identification from Imperfect Data | null | Journal Of Artificial Intelligence Research, Volume 32, pages
825-877, 2008 | 10.1613/jair.2374 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experience in the physical sciences suggests that the only realistic means of
understanding complex systems is through the use of mathematical models.
Typically, this has come to mean the identification of quantitative models
expressed as differential equations. Quantitative modelling works best when the
structure of the model (i.e., the form of the equations) is known; and the
primary concern is one of estimating the values of the parameters in the model.
For complex biological systems, the model-structure is rarely known and the
modeler has to deal with both model-identification and parameter-estimation. In
this paper we are concerned with providing automated assistance to the first of
these problems. Specifically, we examine the identification by machine of the
structural relationships between experimentally observed variables. These
relationship will be expressed in the form of qualitative abstractions of a
quantitative model. Such qualitative models may not only provide clues to the
precise quantitative model, but also assist in understanding the essence of
that model. Our position in this paper is that background knowledge
incorporating system modelling principles can be used to constrain effectively
the set of good qualitative models. Utilising the model-identification
framework provided by Inductive Logic Programming (ILP) we present empirical
support for this position using a series of increasingly complex artificial
datasets. The results are obtained with qualitative and quantitative data
subject to varying amounts of noise and different degrees of sparsity. The
results also point to the presence of a set of qualitative states, which we
term kernel subsets, that may be necessary for a qualitative model-learner to
learn correct models. We demonstrate scalability of the method to biological
system modelling by identification of the glycolysis metabolic pathway from
data.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:02:30 GMT"
}
] | 1,320,192,000,000 | [
[
"Coghill",
"George M.",
""
],
[
"King",
"Ross D.",
""
],
[
"Srinivasan",
"Ashwin",
""
]
] |
1111.0053 | Malcolm Ross Kinsella Ryan | Malcolm Ross Kinsella Ryan | Exploiting Subgraph Structure in Multi-Robot Path Planning | null | Journal Of Artificial Intelligence Research, Volume 31, pages
497-542, 2008 | 10.1613/jair.2408 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-robot path planning is difficult due to the combinatorial explosion of
the search space with every new robot added. Complete search of the combined
state-space soon becomes intractable. In this paper we present a novel form of
abstraction that allows us to plan much more efficiently. The key to this
abstraction is the partitioning of the map into subgraphs of known structure
with entry and exit restrictions which we can represent compactly. Planning
then becomes a search in the much smaller space of subgraph configurations.
Once an abstract plan is found, it can be quickly resolved into a correct (but
possibly sub-optimal) concrete plan without the need for further search. We
prove that this technique is sound and complete and demonstrate its practical
effectiveness on a real map.
A contending solution, prioritised planning, is also evaluated and shown to
have similar performance albeit at the cost of completeness. The two approaches
are not necessarily conflicting; we demonstrate how they can be combined into a
single algorithm which outperforms either approach alone.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:09:48 GMT"
}
] | 1,320,192,000,000 | [
[
"Ryan",
"Malcolm Ross Kinsella",
""
]
] |
1111.0055 | Anastasia Analyti | Anastasia Analyti, Grigoris Antoniou, Carlos Viegas Dam\'asio, Gerd
Wagner | Extended RDF as a Semantic Foundation of Rule Markup Languages | null | Journal Of Artificial Intelligence Research, Volume 32, pages
37-94, 2008 | 10.1613/jair.2425 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontologies and automated reasoning are the building blocks of the Semantic
Web initiative. Derivation rules can be included in an ontology to define
derived concepts, based on base concepts. For example, rules allow to define
the extension of a class or property, based on a complex relation between the
extensions of the same or other classes and properties. On the other hand, the
inclusion of negative information both in the form of negation-as-failure and
explicit negative information is also needed to enable various forms of
reasoning. In this paper, we extend RDF graphs with weak and strong negation,
as well as derivation rules. The ERDF stable model semantics of the extended
framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive
feature of our theory, which is based on Partial Logic, is that both truth and
falsity extensions of properties and classes are considered, allowing for truth
value gaps. Our framework supports both closed-world and open-world reasoning
through the explicit representation of the particular closed-world assumptions
and the ERDF ontological categories of total properties and total classes.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:11:46 GMT"
}
] | 1,320,192,000,000 | [
[
"Analyti",
"Anastasia",
""
],
[
"Antoniou",
"Grigoris",
""
],
[
"Damásio",
"Carlos Viegas",
""
],
[
"Wagner",
"Gerd",
""
]
] |
1111.0056 | Omer Gim\'enez | Omer Gim\'enez, Anders Jonsson | The Complexity of Planning Problems With Simple Causal Graphs | null | Journal Of Artificial Intelligence Research, Volume 31, pages
319-351, 2008 | 10.1613/jair.2432 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present three new complexity results for classes of planning problems with
simple causal graphs. First, we describe a polynomial-time algorithm that uses
macros to generate plans for the class 3S of planning problems with binary
state variables and acyclic causal graphs. This implies that plan generation
may be tractable even when a planning problem has an exponentially long minimal
solution. We also prove that the problem of plan existence for planning
problems with multi-valued variables and chain causal graphs is NP-hard.
Finally, we show that plan existence for planning problems with binary state
variables and polytree causal graphs is NP-complete.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:12:22 GMT"
}
] | 1,320,192,000,000 | [
[
"Giménez",
"Omer",
""
],
[
"Jonsson",
"Anders",
""
]
] |
1111.0059 | Subbarao Kambhampati | Menkes Hector Louis van den Briel, Thomas Vossen, Subbarao Kambhampati | Loosely Coupled Formulations for Automated Planning: An Integer
Programming Perspective | null | Journal Of Artificial Intelligence Research, Volume 31, pages
217-257, 2008 | 10.1613/jair.2443 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We represent planning as a set of loosely coupled network flow problems,
where each network corresponds to one of the state variables in the planning
domain. The network nodes correspond to the state variable values and the
network arcs correspond to the value transitions. The planning problem is to
find a path (a sequence of actions) in each network such that, when merged,
they constitute a feasible plan. In this paper we present a number of integer
programming formulations that model these loosely coupled networks with varying
degrees of flexibility. Since merging may introduce exponentially many ordering
constraints we implement a so-called branch-and-cut algorithm, in which these
constraints are dynamically generated and added to the formulation when needed.
Our results are very promising, they improve upon previous planning as integer
programming approaches and lay the foundation for integer programming
approaches for cost optimal planning.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:16:02 GMT"
}
] | 1,320,192,000,000 | [
[
"Briel",
"Menkes Hector Louis van den",
""
],
[
"Vossen",
"Thomas",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1111.0060 | J. Christopher Beck | Daria Terekhov, J. Christopher Beck | A Constraint Programming Approach for Solving a Queueing Control Problem | null | Journal Of Artificial Intelligence Research, Volume 32, pages
123-167, 2008 | 10.1613/jair.2446 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a facility with front room and back room operations, it is useful to
switch workers between the rooms in order to cope with changing customer
demand. Assuming stochastic customer arrival and service times, we seek a
policy for switching workers such that the expected customer waiting time is
minimized while the expected back room staffing is sufficient to perform all
work. Three novel constraint programming models and several shaving procedures
for these models are presented. Experimental results show that a model based on
closed-form expressions together with a combination of shaving procedures is
the most efficient. This model is able to find and prove optimal solutions for
many problem instances within a reasonable run-time. Previously, the only
available approach was a heuristic algorithm. Furthermore, a hybrid method
combining the heuristic and the best constraint programming method is shown to
perform as well as the heuristic in terms of solution quality over time, while
achieving the same performance in terms of proving optimality as the pure
constraint programming model. This is the first work of which we are aware that
solves such queueing-based problems with constraint programming.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:16:41 GMT"
}
] | 1,320,192,000,000 | [
[
"Terekhov",
"Daria",
""
],
[
"Beck",
"J. Christopher",
""
]
] |
1111.0062 | Frans A. Oliehoek | Frans A. Oliehoek, Matthijs T. J. Spaan, Nikos Vlassis | Optimal and Approximate Q-value Functions for Decentralized POMDPs | null | Journal Of Artificial Intelligence Research, Volume 32, pages
289-353, 2008 | 10.1613/jair.2447 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision-theoretic planning is a popular approach to sequential decision
making problems, because it treats uncertainty in sensing and acting in a
principled way. In single-agent frameworks like MDPs and POMDPs, planning can
be carried out by resorting to Q-value functions: an optimal Q-value function
Q* is computed in a recursive manner by dynamic programming, and then an
optimal policy is extracted from Q*. In this paper we study whether similar
Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs),
and how policies can be extracted from such value functions. We define two
forms of the optimal Q-value function for Dec-POMDPs: one that gives a
normative description as the Q-value function of an optimal pure joint policy
and another one that is sequentially rational and thus gives a recipe for
computation. This computation, however, is infeasible for all but the smallest
problems. Therefore, we analyze various approximate Q-value functions that
allow for efficient computation. We describe how they relate, and we prove that
they all provide an upper bound to the optimal Q-value function Q*. Finally,
unifying some previous approaches for solving Dec-POMDPs, we describe a family
of algorithms for extracting policies from such Q-value functions, and perform
an experimental evaluation on existing test problems, including a new
firefighting benchmark problem.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:17:44 GMT"
}
] | 1,320,192,000,000 | [
[
"Oliehoek",
"Frans A.",
""
],
[
"Spaan",
"Matthijs T. J.",
""
],
[
"Vlassis",
"Nikos",
""
]
] |
1111.0065 | Claudia V. Goldman | Claudia V. Goldman, Shlomo Zilberstein | Communication-Based Decomposition Mechanisms for Decentralized MDPs | null | Journal Of Artificial Intelligence Research, Volume 32, pages
169-202, 2008 | 10.1613/jair.2466 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-agent planning in stochastic environments can be framed formally as a
decentralized Markov decision problem. Many real-life distributed problems that
arise in manufacturing, multi-robot coordination and information gathering
scenarios can be formalized using this framework. However, finding the optimal
solution in the general case is hard, limiting the applicability of recently
developed algorithms. This paper provides a practical approach for solving
decentralized control problems when communication among the decision makers is
possible, but costly. We develop the notion of communication-based mechanism
that allows us to decompose a decentralized MDP into multiple single-agent
problems. In this framework, referred to as decentralized semi-Markov decision
process with direct communication (Dec-SMDP-Com), agents operate separately
between communications. We show that finding an optimal mechanism is equivalent
to solving optimally a Dec-SMDP-Com. We also provide a heuristic search
algorithm that converges on the optimal decomposition. Restricting the
decomposition to some specific types of local behaviors reduces significantly
the complexity of planning. In particular, we present a polynomial-time
algorithm for the case in which individual agents perform goal-oriented
behaviors between communications. The paper concludes with an additional
tractable algorithm that enables the introduction of human knowledge, thereby
reducing the overall problem to finding the best time to communicate. Empirical
results show that these approaches provide good approximate solutions.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:22:03 GMT"
}
] | 1,320,192,000,000 | [
[
"Goldman",
"Claudia V.",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1111.0067 | Joseph Culberson | Fan Yang, Joseph Culberson, Robert Holte, Uzi Zahavi, Ariel Felner | A General Theory of Additive State Space Abstractions | null | Journal Of Artificial Intelligence Research, Volume 32, pages
631-662, 2008 | 10.1613/jair.2486 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Informally, a set of abstractions of a state space S is additive if the
distance between any two states in S is always greater than or equal to the sum
of the corresponding distances in the abstract spaces. The first known additive
abstractions, called disjoint pattern databases, were experimentally
demonstrated to produce state of the art performance on certain state spaces.
However, previous applications were restricted to state spaces with special
properties, which precludes disjoint pattern databases from being defined for
several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake
puzzle. In this paper we give a general definition of additive abstractions
that can be applied to any state space and prove that heuristics based on
additive abstractions are consistent as well as admissible. We use this new
definition to create additive abstractions for these testbeds and show
experimentally that well chosen additive abstractions can reduce search time
substantially for the (18,4)-TopSpin puzzle and by three orders of magnitude
over state of the art methods for the 17-Pancake puzzle. We also derive a way
of testing if the heuristic value returned by additive abstractions is provably
too low and show that the use of this test can reduce search time for the
15-puzzle and TopSpin by roughly a factor of two.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:26:44 GMT"
}
] | 1,320,192,000,000 | [
[
"Yang",
"Fan",
""
],
[
"Culberson",
"Joseph",
""
],
[
"Holte",
"Robert",
""
],
[
"Zahavi",
"Uzi",
""
],
[
"Felner",
"Ariel",
""
]
] |
1111.0068 | Saket Joshi | Chenggang Wang, Saket Joshi, Roni Khardon | First Order Decision Diagrams for Relational MDPs | null | Journal Of Artificial Intelligence Research, Volume 31, pages
431-472, 2008 | 10.1613/jair.2489 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markov decision processes capture sequential decision making under
uncertainty, where an agent must choose actions so as to optimize long term
reward. The paper studies efficient reasoning mechanisms for Relational Markov
Decision Processes (RMDP) where world states have an internal relational
structure that can be naturally described in terms of objects and relations
among them. Two contributions are presented. First, the paper develops First
Order Decision Diagrams (FODD), a new compact representation for functions over
relational structures, together with a set of operators to combine FODDs, and
novel reduction techniques to keep the representation small. Second, the paper
shows how FODDs can be used to develop solutions for RMDPs, where reasoning is
performed at the abstract level and the resulting optimal policy is independent
of domain size (number of objects) or instantiation. In particular, a variant
of the value iteration algorithm is developed by using special operations over
FODDs, and the algorithm is shown to converge to the optimal policy.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:27:57 GMT"
}
] | 1,320,192,000,000 | [
[
"Wang",
"Chenggang",
""
],
[
"Joshi",
"Saket",
""
],
[
"Khardon",
"Roni",
""
]
] |
1111.0860 | E. Giunchiglia | E. Giunchiglia, M. Narizzano, A. Tacchella | Clause/Term Resolution and Learning in the Evaluation of Quantified
Boolean Formulas | null | Journal Of Artificial Intelligence Research, Volume 26, pages
371-416, 2006 | 10.1613/jair.1959 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resolution is the rule of inference at the basis of most procedures for
automated reasoning. In these procedures, the input formula is first translated
into an equisatisfiable formula in conjunctive normal form (CNF) and then
represented as a set of clauses. Deduction starts by inferring new clauses by
resolution, and goes on until the empty clause is generated or satisfiability
of the set of clauses is proven, e.g., because no new clauses can be generated.
In this paper, we restrict our attention to the problem of evaluating
Quantified Boolean Formulas (QBFs). In this setting, the above outlined
deduction process is known to be sound and complete if given a formula in CNF
and if a form of resolution, called Q-resolution, is used. We introduce
Q-resolution on terms, to be used for formulas in disjunctive normal form. We
show that the computation performed by most of the available procedures for
QBFs --based on the Davis-Logemann-Loveland procedure (DLL) for propositional
satisfiability-- corresponds to a tree in which Q-resolution on terms and
clauses alternate. This poses the theoretical bases for the introduction of
learning, corresponding to recording Q-resolution formulas associated with the
nodes of the tree. We discuss the problems related to the introduction of
learning in DLL based procedures, and present solutions extending
state-of-the-art proposals coming from the literature on propositional
satisfiability. Finally, we show that our DLL based solver extended with
learning, performs significantly better on benchmarks used in the 2003 QBF
solvers comparative evaluation.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2011 18:43:49 GMT"
}
] | 1,320,364,800,000 | [
[
"Giunchiglia",
"E.",
""
],
[
"Narizzano",
"M.",
""
],
[
"Tacchella",
"A.",
""
]
] |
1111.1321 | Oleg Varlamov Oleg | Oleg O. Varlamov | MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and
Practical Realization of Automated Constructor of Algorithms Handling More
than Three Million Production Rules | 23 pages, 21 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theoretical transition from the graphs of production systems to the
bipartite graphs of the MIVAR nets is shown. Examples of the implementation of
the MIVAR nets in the formalisms of matrixes and graphs are given. The linear
computational complexity of algorithms for automated building of objects and
rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets
the UDAV software complex is developed, handling more than 1.17 million objects
and more than 3.5 million rules on ordinary computers. The results of
experiments that confirm a linear computational complexity of the MIVAR method
of information processing are given.
Keywords: MIVAR, MIVAR net, logical inference, computational complexity,
artificial intelligence, intelligent systems, expert systems, General Problem
Solver.
| [
{
"version": "v1",
"created": "Sat, 5 Nov 2011 15:26:15 GMT"
}
] | 1,320,710,400,000 | [
[
"Varlamov",
"Oleg O.",
""
]
] |
1111.1486 | Yisong Wang | Yisong Wang and Jia-Huai You and Li Yan Yuan and Yi-Dong Shen and
Thomas Eiter | Embedding Description Logic Programs into Default Logic | 53 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Description logic programs (dl-programs) under the answer set semantics
formulated by Eiter {\em et al.} have been considered as a prominent formalism
for integrating rules and ontology knowledge bases. A question of interest has
been whether dl-programs can be captured in a general formalism of nonmonotonic
logic. In this paper, we study the possibility of embedding dl-programs into
default logic. We show that dl-programs under the strong and weak answer set
semantics can be embedded in default logic by combining two translations, one
of which eliminates the constraint operator from nonmonotonic dl-atoms and the
other translates a dl-program into a default theory. For dl-programs without
nonmonotonic dl-atoms but with the negation-as-failure operator, our embedding
is polynomial, faithful, and modular. In addition, our default logic encoding
can be extended in a simple way to capture recently proposed weakly
well-supported answer set semantics, for arbitrary dl-programs. These results
reinforce the argument that default logic can serve as a fruitful foundation
for query-based approaches to integrating ontology and rules. With its simple
syntax and intuitive semantics, plus available computational results, default
logic can be considered an attractive approach to integration of ontology and
rules.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2011 04:39:56 GMT"
}
] | 1,320,710,400,000 | [
[
"Wang",
"Yisong",
""
],
[
"You",
"Jia-Huai",
""
],
[
"Yuan",
"Li Yan",
""
],
[
"Shen",
"Yi-Dong",
""
],
[
"Eiter",
"Thomas",
""
]
] |
1111.1941 | Jean Vincent Fonou Dombeu | Jean Vincent Fonou-Dombeu and Magda Huisman | Semantic-Driven e-Government: Application of Uschold and King Ontology
Building Methodology for Semantic Ontology Models Development | 20 pages, 6 figures | International Journal of Web & Semantic Technology (IJWesT) Vol.
2, No. 4, October 2011, 1-20 | 10.5121/ijwest.2011.2401 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electronic government (e-government) has been one of the most active areas of
ontology development during the past six years. In e-government, ontologies are
being used to describe and specify e-government services (e-services) because
they enable easy composition, matching, mapping and merging of various
e-government services. More importantly, they also facilitate the semantic
integration and interoperability of e-government services. However, it is still
unclear in the current literature how an existing ontology building methodology
can be applied to develop semantic ontology models in a government service
domain. In this paper the Uschold and King ontology building methodology is
applied to develop semantic ontology models in a government service domain.
Firstly, the Uschold and King methodology is presented, discussed and applied
to build a government domain ontology. Secondly, the domain ontology is
evaluated for semantic consistency using its semi-formal representation in
Description Logic. Thirdly, an alignment of the domain ontology with the
Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) upper
level ontology is drawn to allow its wider visibility and facilitate its
integration with existing metadata standard. Finally, the domain ontology is
formally written in Web Ontology Language (OWL) to enable its automatic
processing by computers. The study aims to provide direction for the
application of existing ontology building methodologies in the Semantic Web
development processes of e-government domain specific ontology models; which
would enable their repeatability in other e-government projects and strengthen
the adoption of semantic technologies in e-government.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2011 15:40:10 GMT"
}
] | 1,320,796,800,000 | [
[
"Fonou-Dombeu",
"Jean Vincent",
""
],
[
"Huisman",
"Magda",
""
]
] |
1111.2249 | H. H. Hoos | Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown | SATzilla: Portfolio-based Algorithm Selection for SAT | null | Journal Of Artificial Intelligence Research, Volume 32, pages
565-606, 2008 | 10.1613/jair.2490 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been widely observed that there is no single "dominant" SAT solver;
instead, different solvers perform best on different instances. Rather than
following the traditional approach of choosing the best solver for a given
class of instances, we advocate making this decision online on a per-instance
basis. Building on previous work, we describe SATzilla, an automated approach
for constructing per-instance algorithm portfolios for SAT that use so-called
empirical hardness models to choose among their constituent solvers. This
approach takes as input a distribution of problem instances and a set of
component solvers, and constructs a portfolio optimizing a given objective
function (such as mean runtime, percent of instances solved, or score in a
competition). The excellent performance of SATzilla was independently verified
in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one
silver and one bronze medal. In this article, we go well beyond SATzilla07 by
making the portfolio construction scalable and completely automated, and
improving it by integrating local search solvers as candidate solvers, by
predicting performance score instead of runtime, and by using hierarchical
hardness models that take into account different types of SAT instances. We
demonstrate the effectiveness of these new techniques in extensive experimental
results on data sets including instances from the most recent SAT competition.
| [
{
"version": "v1",
"created": "Mon, 31 Oct 2011 22:28:59 GMT"
}
] | 1,320,883,200,000 | [
[
"Xu",
"Lin",
""
],
[
"Hutter",
"Frank",
""
],
[
"Hoos",
"Holger H.",
""
],
[
"Leyton-Brown",
"Kevin",
""
]
] |
1111.2763 | Nicolaie Popescu-Bodorin | N. Popescu-Bodorin, V.E. Balas, I.M. Motoc | 8-Valent Fuzzy Logic for Iris Recognition and Biometry | 6 pages, 2 figures, 5th IEEE Int. Symp. on Computational Intelligence
and Intelligent Informatics (Floriana, Malta, September 15-17), ISBN:
978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print), 2011 | Proc. 5th IEEE Int. Symp. on Computational Intelligence and
Intelligent Informatics, pp. 149-154, ISBN: 978-1-4577-1861-8 (electronic),
978-1-4577-1860-1 (print), IEEE Press, 2011 | 10.1109/ISCIII.2011.6069761 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper shows that maintaining logical consistency of an iris recognition
system is a matter of finding a suitable partitioning of the input space in
enrollable and unenrollable pairs by negotiating the user comfort and the
safety of the biometric system. In other words, consistent enrollment is
mandatory in order to preserve system consistency. A fuzzy 3-valued
disambiguated model of iris recognition is proposed and analyzed in terms of
completeness, consistency, user comfort and biometric safety. It is also shown
here that the fuzzy 3-valued model of iris recognition is hosted by an 8-valued
Boolean algebra of modulo 8 integers that represents the computational
formalization in which a biometric system (a software agent) can achieve the
artificial understanding of iris recognition in a logically consistent manner.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2011 21:38:25 GMT"
}
] | 1,321,228,800,000 | [
[
"Popescu-Bodorin",
"N.",
""
],
[
"Balas",
"V. E.",
""
],
[
"Motoc",
"I. M.",
""
]
] |
1111.3690 | Nicolas Maudet | Yann Chevaleyre, J\'er\^ome Lang, Nicolas Maudet, J\'er\^ome Monnot,
Lirong Xia | New Candidates Welcome! Possible Winners with respect to the Addition of
New Candidates | 34 pages | Mathematical Social Sciences 64(1), 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In voting contexts, some new candidates may show up in the course of the
process. In this case, we may want to determine which of the initial candidates
are possible winners, given that a fixed number $k$ of new candidates will be
added. We give a computational study of this problem, focusing on scoring
rules, and we provide a formal comparison with related problems such as control
via adding candidates or cloning.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2011 23:41:11 GMT"
}
] | 1,424,131,200,000 | [
[
"Chevaleyre",
"Yann",
""
],
[
"Lang",
"Jérôme",
""
],
[
"Maudet",
"Nicolas",
""
],
[
"Monnot",
"Jérôme",
""
],
[
"Xia",
"Lirong",
""
]
] |
1111.3934 | Bill Hibbard | Bill Hibbard | Model-based Utility Functions | 24 pages, extensive revisions | Journal of Artificial General Intelligence 3(1) 1-24, 2012 | 10.2478/v10229-011-0013-5 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Orseau and Ring, as well as Dewey, have recently described problems,
including self-delusion, with the behavior of agents using various definitions
of utility functions. An agent's utility function is defined in terms of the
agent's history of interactions with its environment. This paper argues, via
two examples, that the behavior problems can be avoided by formulating the
utility function in two steps: 1) inferring a model of the environment from
interactions, and 2) computing utility as a function of the environment model.
Basing a utility function on a model that the agent must learn implies that the
utility function must initially be expressed in terms of specifications to be
matched to structures in the learned model. These specifications constitute
prior assumptions about the environment so this approach will not work with
arbitrary environments. But the approach should work for agents designed by
humans to act in the physical world. The paper also addresses the issue of
self-modifying agents and shows that if provided with the possibility to modify
their utility functions agents will not choose to do so, under some usual
assumptions.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2011 20:13:54 GMT"
},
{
"version": "v2",
"created": "Sat, 12 May 2012 16:05:46 GMT"
}
] | 1,337,040,000,000 | [
[
"Hibbard",
"Bill",
""
]
] |
1111.4083 | Denis Berthier | Denis Berthier (DSI) | Unbiased Statistics of a CSP - A Controlled-Bias Generator | null | Innovations in Computing Sciences and Software Engineering, (2010)
165-170 | 10.1007/978-90-481-3660-5_28 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that estimating the complexity (mean and distribution) of the
instances of a fixed size Constraint Satisfaction Problem (CSP) can be very
hard. We deal with the main two aspects of the problem: defining a measure of
complexity and generating random unbiased instances. For the first problem, we
rely on a general framework and a measure of complexity we presented at
CISSE08. For the generation problem, we restrict our analysis to the Sudoku
example and we provide a solution that also explains why it is so difficult.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2011 13:15:24 GMT"
}
] | 1,433,289,600,000 | [
[
"Berthier",
"Denis",
"",
"DSI"
]
] |
1111.4232 | Kirill Sorudeykin Mr | Kirill A. Sorudeykin | A Model of Spatial Thinking for Computational Intelligence | 8 pages, 5 figures; IEEE East-West Design & Test Symposium, 2011 | null | 10.1109/EWDTS.2011.6116427 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trying to be effective (no matter who exactly and in what field) a person
face the problem which inevitably destroys all our attempts to easily get to a
desired goal. The problem is the existence of some insuperable barriers for our
mind, anotherwords barriers for principles of thinking. They are our clue and
main reason for research. Here we investigate these barriers and their features
exposing the nature of mental process. We start from special structures which
reflect the ways to define relations between objects. Then we came to realizing
about what is the material our mind uses to build thoughts, to make
conclusions, to understand, to form reasoning, etc. This can be called a mental
dynamics. After this the nature of mental barriers on the required level of
abstraction as well as the ways to pass through them became clear. We begin to
understand why thinking flows in such a way, with such specifics and with such
limitations we can observe in reality. This can help us to be more optimal. At
the final step we start to understand, what ma-thematical models can be applied
to such a picture. We start to express our thoughts in a language of
mathematics, developing an apparatus for our Spatial Theory of Mind, suitable
to represent processes and infrastructure of thinking. We use abstract algebra
and stay invariant in relation to the nature of objects.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2011 22:22:21 GMT"
}
] | 1,479,340,800,000 | [
[
"Sorudeykin",
"Kirill A.",
""
]
] |
1111.5689 | Mehdi Kaytoue | Mehdi Kaytoue (INRIA Lorraine - LORIA), Sergei O. Kuznetsov, Amedeo
Napoli (INRIA Lorraine - LORIA) | Revisiting Numerical Pattern Mining with Formal Concept Analysis | null | International Joint Conference on Artificial Intelligence (IJCAI)
(2011) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the problem of mining numerical data in the
framework of Formal Concept Analysis. The usual way is to use a scaling
procedure --transforming numerical attributes into binary ones-- leading either
to a loss of information or of efficiency, in particular w.r.t. the volume of
extracted patterns. By contrast, we propose to directly work on numerical data
in a more precise and efficient way, and we prove it. For that, the notions of
closed patterns, generators and equivalent classes are revisited in the
numerical context. Moreover, two original algorithms are proposed and used in
an evaluation involving real-world data, showing the predominance of the
present approach.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2011 07:55:16 GMT"
}
] | 1,322,438,400,000 | [
[
"Kaytoue",
"Mehdi",
"",
"INRIA Lorraine - LORIA"
],
[
"Kuznetsov",
"Sergei O.",
"",
"INRIA Lorraine - LORIA"
],
[
"Napoli",
"Amedeo",
"",
"INRIA Lorraine - LORIA"
]
] |
1111.6117 | Marcus Hutter | Peter Sunehag and Marcus Hutter | Principles of Solomonoff Induction and AIXI | 14 LaTeX pages | Proc. Solomonoff 85th Memorial Conference (SOL 2011) pages 386-398 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We identify principles characterizing Solomonoff Induction by demands on an
agent's external behaviour. Key concepts are rationality, computability,
indifference and time consistency. Furthermore, we discuss extensions to the
full AI case to derive AIXI.
| [
{
"version": "v1",
"created": "Fri, 25 Nov 2011 21:35:29 GMT"
}
] | 1,405,382,400,000 | [
[
"Sunehag",
"Peter",
""
],
[
"Hutter",
"Marcus",
""
]
] |
1111.6191 | Albrecht Zimmermann | Bj\"orn Bringmann and Siegfried Nijssen and Albrecht Zimmermann | Pattern-Based Classification: A Unifying Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of patterns in predictive models is a topic that has received a lot
of attention in recent years. Pattern mining can help to obtain models for
structured domains, such as graphs and sequences, and has been proposed as a
means to obtain more accurate and more interpretable models. Despite the large
amount of publications devoted to this topic, we believe however that an
overview of what has been accomplished in this area is missing. This paper
presents our perspective on this evolving area. We identify the principles of
pattern mining that are important when mining patterns for models and provide
an overview of pattern-based classification methods. We categorize these
methods along the following dimensions: (1) whether they post-process a
pre-computed set of patterns or iteratively execute pattern mining algorithms;
(2) whether they select patterns model-independently or whether the pattern
selection is guided by a model. We summarize the results that have been
obtained for each of these methods.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2011 20:11:56 GMT"
}
] | 1,322,524,800,000 | [
[
"Bringmann",
"Björn",
""
],
[
"Nijssen",
"Siegfried",
""
],
[
"Zimmermann",
"Albrecht",
""
]
] |
1111.6401 | Hajar Elmaghraoui | Hajar Elmaghraoui, Imane Zaoui, Dalila Chiadmi, Laila Benhlima | Graph based E-Government web service composition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, e-government has emerged as a government policy to improve the
quality and efficiency of public administrations. By exploiting the potential
of new information and communication technologies, government agencies are
providing a wide spectrum of online services. These services are composed of
several web services that comply with well defined processes. One of the big
challenges is the need to optimize the composition of the elementary web
services. In this paper, we present a solution for optimizing the computation
effort in web service composition. Our method is based on Graph Theory. We
model the semantic relationship between the involved web services through a
directed graph. Then, we compute all shortest paths using for the first time,
an extended version of the Floyd-Warshall algorithm.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2011 10:52:28 GMT"
}
] | 1,322,524,800,000 | [
[
"Elmaghraoui",
"Hajar",
""
],
[
"Zaoui",
"Imane",
""
],
[
"Chiadmi",
"Dalila",
""
],
[
"Benhlima",
"Laila",
""
]
] |
1111.6713 | Mohammed Elmogy Dr. | Ahmed Tolba and Nabila Eladawi and Mohammed Elmogy | An Enhanced Indexing And Ranking Technique On The Semantic Web | 8 pages, 7 figures | IJCSI International Journal of Computer Science Issues, Vol. 8,
Issue 5, No 3, 2011, 118-125 | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | With the fast growth of the Internet, more and more information is available
on the Web. The Semantic Web has many features which cannot be handled by using
the traditional search engines. It extracts metadata for each discovered Web
documents in RDF or OWL formats, and computes relations between documents. We
proposed a hybrid indexing and ranking technique for the Semantic Web which
finds relevant documents and computes the similarity among a set of documents.
First, it returns with the most related document from the repository of
Semantic Web Documents (SWDs) by using a modified version of the ObjectRank
technique. Then, it creates a sub-graph for the most related SWDs. Finally, It
returns the hubs and authorities of these document by using the HITS algorithm.
Our technique increases the quality of the results and decreases the execution
time of processing the user's query.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2011 07:24:27 GMT"
}
] | 1,322,611,200,000 | [
[
"Tolba",
"Ahmed",
""
],
[
"Eladawi",
"Nabila",
""
],
[
"Elmogy",
"Mohammed",
""
]
] |
1111.6790 | Sao Mai Nguyen | Sao Mai Nguyen (INRIA Bordeaux - Sud-Ouest), Adrien Baranes (INRIA
Bordeaux - Sud-Ouest), Pierre-Yves Oudeyer (INRIA Bordeaux - Sud-Ouest) | Constraining the Size Growth of the Task Space with Socially Guided
Intrinsic Motivation using Demonstrations | JCAI Workshop on Agents Learning Interactively from Human Teachers
(ALIHT), Barcelona : Spain (2011) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2011 12:29:27 GMT"
}
] | 1,322,611,200,000 | [
[
"Nguyen",
"Sao Mai",
"",
"INRIA Bordeaux - Sud-Ouest"
],
[
"Baranes",
"Adrien",
"",
"INRIA\n Bordeaux - Sud-Ouest"
],
[
"Oudeyer",
"Pierre-Yves",
"",
"INRIA Bordeaux - Sud-Ouest"
]
] |
1112.0508 | Weiwei Cheng | Weiwei Cheng, Eyke H\"ullermeier | Label Ranking with Abstention: Predicting Partial Orders by Thresholding
Probability Distributions (Extended Abstract) | 4 pages, 1 figure, appeared at NIPS 2011 Choice Models and Preference
Learning workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an extension of the setting of label ranking, in which the
learner is allowed to make predictions in the form of partial instead of total
orders. Predictions of that kind are interpreted as a partial abstention: If
the learner is not sufficiently certain regarding the relative order of two
alternatives, it may abstain from this decision and instead declare these
alternatives as being incomparable. We propose a new method for learning to
predict partial orders that improves on an existing approach, both
theoretically and empirically. Our method is based on the idea of thresholding
the probabilities of pairwise preferences between labels as induced by a
predicted (parameterized) probability distribution on the set of all rankings.
| [
{
"version": "v1",
"created": "Fri, 2 Dec 2011 17:09:43 GMT"
}
] | 1,323,043,200,000 | [
[
"Cheng",
"Weiwei",
""
],
[
"Hüllermeier",
"Eyke",
""
]
] |
1112.1489 | Wan-Li Chen | Wan-Li Chen | Multi-granular Perspectives on Covering | 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Covering model provides a general framework for granular computing in that
overlapping among granules are almost indispensable. For any given covering,
both intersection and union of covering blocks containing an element are
exploited as granules to form granular worlds at different abstraction levels,
respectively, and transformations among these different granular worlds are
also discussed. As an application of the presented multi-granular perspective
on covering, relational interpretation and axiomization of four types of
covering based rough upper approximation operators are investigated, which can
be dually applied to lower ones.
| [
{
"version": "v1",
"created": "Wed, 7 Dec 2011 07:11:56 GMT"
}
] | 1,323,302,400,000 | [
[
"Chen",
"Wan-Li",
""
]
] |
1112.2113 | Varun Raj Kompella | Varun Raj Kompella, Matthew Luciw and Juergen Schmidhuber | Incremental Slow Feature Analysis: Adaptive and Episodic Learning from
High-Dimensional Input Streams | null | Neural Computation, 2012, Vol. 24, No. 11, Pages 2994-3024 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Slow Feature Analysis (SFA) extracts features representing the underlying
causes of changes within a temporally coherent high-dimensional raw sensory
input signal. Our novel incremental version of SFA (IncSFA) combines
incremental Principal Components Analysis and Minor Components Analysis. Unlike
standard batch-based SFA, IncSFA adapts along with non-stationary environments,
is amenable to episodic training, is not corrupted by outliers, and is
covariance-free. These properties make IncSFA a generally useful unsupervised
preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA
and MCA updates take the form of Hebbian and anti-Hebbian updating, extending
the biological plausibility of SFA. In both single node and deep network
versions, IncSFA learns to encode its input streams (such as high-dimensional
video) by informative slow features representing meaningful abstract
environmental properties. It can handle cases where batch SFA fails.
| [
{
"version": "v1",
"created": "Fri, 9 Dec 2011 15:01:25 GMT"
}
] | 1,349,913,600,000 | [
[
"Kompella",
"Varun Raj",
""
],
[
"Luciw",
"Matthew",
""
],
[
"Schmidhuber",
"Juergen",
""
]
] |
1112.2640 | C\`esar Ferri | Jos\'e Hern\'andez-Orallo, Peter Flach, C\`esar Ferri | Threshold Choice Methods: the Missing Link | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many performance metrics have been introduced for the evaluation of
classification performance, with different origins and niches of application:
accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the
absolute error, and the Brier score (with its decomposition into refinement and
calibration). One way of understanding the relation among some of these metrics
is the use of variable operating conditions (either in the form of
misclassification costs or class proportions). Thus, a metric may correspond to
some expected loss over a range of operating conditions. One dimension for the
analysis has been precisely the distribution we take for this range of
operating conditions, leading to some important connections in the area of
proper scoring rules. However, we show that there is another dimension which
has not received attention in the analysis of performance metrics. This new
dimension is given by the decision rule, which is typically implemented as a
threshold choice method when using scoring models. In this paper, we explore
many old and new threshold choice methods: fixed, score-uniform, score-driven,
rate-driven and optimal, among others. By calculating the loss of these methods
for a uniform range of operating conditions we get the 0-1 loss, the absolute
error, the Brier score (mean squared error), the AUC and the refinement loss
respectively. This provides a comprehensive view of performance metrics as well
as a systematic approach to loss minimisation, namely: take a model, apply
several threshold choice methods consistent with the information which is (and
will be) available about the operating condition, and compare their expected
losses. In order to assist in this procedure we also derive several connections
between the aforementioned performance metrics, and we highlight the role of
calibration in choosing the threshold choice method.
| [
{
"version": "v1",
"created": "Mon, 12 Dec 2011 18:03:42 GMT"
},
{
"version": "v2",
"created": "Sat, 28 Jan 2012 09:44:33 GMT"
}
] | 1,327,968,000,000 | [
[
"Hernández-Orallo",
"José",
""
],
[
"Flach",
"Peter",
""
],
[
"Ferri",
"Cèsar",
""
]
] |
1112.2681 | Muhammad Islam | Muhammad Asiful Islam, C. R. Ramakrishnan, I. V. Ramakrishnan | Inference in Probabilistic Logic Programs with Continuous Random
Variables | 12 pages. arXiv admin note: substantial text overlap with
arXiv:1203.4287 | Theory and Practice of Logic Programming / Volume12 / Special
Issue4-5 / July 2012, pp 505-523 | 10.1017/S1471068412000154 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's
PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed
at combining statistical and logical knowledge representation and inference. A
key characteristic of PLP frameworks is that they are conservative extensions
to non-probabilistic logic programs which have been widely used for knowledge
representation. PLP frameworks extend traditional logic programming semantics
to a distribution semantics, where the semantics of a probabilistic logic
program is given in terms of a distribution over possible models of the
program. However, the inference techniques used in these works rely on
enumerating sets of explanations for a query answer. Consequently, these
languages permit very limited use of random variables with continuous
distributions. In this paper, we present a symbolic inference procedure that
uses constraints and represents sets of explanations without enumeration. This
permits us to reason over PLPs with Gaussian or Gamma-distributed random
variables (in addition to discrete-valued random variables) and linear equality
constraints over reals. We develop the inference procedure in the context of
PRISM; however the procedure's core ideas can be easily applied to other PLP
languages as well. An interesting aspect of our inference procedure is that
PRISM's query evaluation process becomes a special case in the absence of any
continuous random variables in the program. The symbolic inference procedure
enables us to reason over complex probabilistic models such as Kalman filters
and a large subclass of Hybrid Bayesian networks that were hitherto not
possible in PLP frameworks. (To appear in Theory and Practice of Logic
Programming).
| [
{
"version": "v1",
"created": "Mon, 12 Dec 2011 20:16:55 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2012 04:28:09 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Oct 2012 03:24:10 GMT"
}
] | 1,349,740,800,000 | [
[
"Islam",
"Muhammad Asiful",
""
],
[
"Ramakrishnan",
"C. R.",
""
],
[
"Ramakrishnan",
"I. V.",
""
]
] |
1112.5381 | Daan Fierens | Daan Fierens | Improving the Efficiency of Approximate Inference for Probabilistic
Logical Models by means of Program Specialization | 17 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the task of performing probabilistic inference with probabilistic
logical models. Many algorithms for approximate inference with such models are
based on sampling. From a logic programming perspective, sampling boils down to
repeatedly calling the same queries on a knowledge base composed of a static
part and a dynamic part. The larger the static part, the more redundancy there
is in these repeated calls. This is problematic since inefficient sampling
yields poor approximations.
We show how to apply logic program specialization to make sampling-based
inference more efficient. We develop an algorithm that specializes the
definitions of the query predicates with respect to the static part of the
knowledge base. In experiments on real-world data we obtain speedups of up to
an order of magnitude, and these speedups grow with the data-size.
| [
{
"version": "v1",
"created": "Thu, 22 Dec 2011 17:01:34 GMT"
}
] | 1,426,723,200,000 | [
[
"Fierens",
"Daan",
""
]
] |
1201.0414 | Xuechong Guan | Xuechong Guan and Yongming Li | Continuity in Information Algebras | null | null | 10.1142/S0218488512500304 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | In this paper, the continuity and strong continuity in domain-free
information algebras and labeled information algebras are introduced
respectively. A more general concept of continuous function which is defined
between two domain-free continuous information algebras is presented. It is
shown that, with the operations combination and focusing, the set of all
continuous functions between two domain-free s-continuous information algebras
forms a new s-continuous information algebra. By studying the relationship
between domain-free information algebras and labeled information algebras, it
is demonstrated that they do correspond to each other on s-compactness.
| [
{
"version": "v1",
"created": "Mon, 2 Jan 2012 02:40:12 GMT"
}
] | 1,349,654,400,000 | [
[
"Guan",
"Xuechong",
""
],
[
"Li",
"Yongming",
""
]
] |
1201.0564 | Toby Walsh | Ronald de Haan, Nina Narodytska, Toby Walsh | The RegularGcc Matrix Constraint | Submitted to CPAIOR 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study propagation of the RegularGcc global constraint. This ensures that
each row of a matrix of decision variables satisfies a Regular constraint, and
each column satisfies a Gcc constraint. On the negative side, we prove that
propagation is NP-hard even under some strong restrictions (e.g. just 3 values,
just 4 states in the automaton, or just 5 columns to the matrix). On the
positive side, we identify two cases where propagation is fixed parameter
tractable. In addition, we show how to improve propagation over a simple
decomposition into separate Regular and Gcc constraints by identifying some
necessary but insufficient conditions for a solution. We enforce these
conditions with some additional weighted row automata. Experimental results
demonstrate the potential of these methods on some standard benchmark problems.
| [
{
"version": "v1",
"created": "Tue, 3 Jan 2012 03:30:18 GMT"
}
] | 1,480,118,400,000 | [
[
"de Haan",
"Ronald",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Walsh",
"Toby",
""
]
] |
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