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1102.0714 | Jose Hernandez-Orallo | Javier Insa-Cabrera, Jose Hernandez-Orallo | An architecture for the evaluation of intelligent systems | 112 pages. In Spanish. Final Project Thesis | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main research areas in Artificial Intelligence is the coding of
agents (programs) which are able to learn by themselves in any situation. This
means that agents must be useful for purposes other than those they were
created for, as, for example, playing chess. In this way we try to get closer
to the pristine goal of Artificial Intelligence. One of the problems to decide
whether an agent is really intelligent or not is the measurement of its
intelligence, since there is currently no way to measure it in a reliable way.
The purpose of this project is to create an interpreter that allows for the
execution of several environments, including those which are generated
randomly, so that an agent (a person or a program) can interact with them. Once
the interaction between the agent and the environment is over, the interpreter
will measure the intelligence of the agent according to the actions, states and
rewards the agent has undergone inside the environment during the test. As a
result we will be able to measure agents' intelligence in any possible
environment, and to make comparisons between several agents, in order to
determine which of them is the most intelligent. In order to perform the tests,
the interpreter must be able to randomly generate environments that are really
useful to measure agents' intelligence, since not any randomly generated
environment will serve that purpose.
| [
{
"version": "v1",
"created": "Thu, 3 Feb 2011 15:58:18 GMT"
}
] | 1,296,777,600,000 | [
[
"Insa-Cabrera",
"Javier",
""
],
[
"Hernandez-Orallo",
"Jose",
""
]
] |
1102.0831 | Madhu G | G.Madhu (1), Dr.A.Govardhan (2), Dr.T.V.Rajinikanth (3) | Intelligent Semantic Web Search Engines: A Brief Survey | null | International journal of Web & Semantic Technology (IJWesT) Vol.2,
No.1, January 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The World Wide Web (WWW) allows the people to share the information (data)
from the large database repositories globally. The amount of information grows
billions of databases. We need to search the information will specialize tools
known generically search engine. There are many of search engines available
today, retrieving meaningful information is difficult. However to overcome this
problem in search engines to retrieve meaningful information intelligently,
semantic web technologies are playing a major role. In this paper we present
survey on the search engine generations and the role of search engines in
intelligent web and semantic search technologies.
| [
{
"version": "v1",
"created": "Fri, 4 Feb 2011 03:56:09 GMT"
}
] | 1,297,036,800,000 | [
[
"Madhu",
"G.",
""
],
[
"Govardhan",
"Dr. A.",
""
],
[
"Rajinikanth",
"Dr. T. V.",
""
]
] |
1102.2670 | Yasin Abbasi-Yadkori Yasin Abbasi-Yadkori | Yasin Abbasi-Yadkori, David Pal, Csaba Szepesvari | Online Least Squares Estimation with Self-Normalized Processes: An
Application to Bandit Problems | Submitted to the 24th Annual Conference on Learning Theory (COLT
2011) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analysis of online least squares estimation is at the heart of many
stochastic sequential decision making problems. We employ tools from the
self-normalized processes to provide a simple and self-contained proof of a
tail bound of a vector-valued martingale. We use the bound to construct a new
tighter confidence sets for the least squares estimate.
We apply the confidence sets to several online decision problems, such as the
multi-armed and the linearly parametrized bandit problems. The confidence sets
are potentially applicable to other problems such as sleeping bandits,
generalized linear bandits, and other linear control problems.
We improve the regret bound of the Upper Confidence Bound (UCB) algorithm of
Auer et al. (2002) and show that its regret is with high-probability a problem
dependent constant. In the case of linear bandits (Dani et al., 2008), we
improve the problem dependent bound in the dimension and number of time steps.
Furthermore, as opposed to the previous result, we prove that our bound holds
for small sample sizes, and at the same time the worst case bound is improved
by a logarithmic factor and the constant is improved.
| [
{
"version": "v1",
"created": "Mon, 14 Feb 2011 04:06:31 GMT"
}
] | 1,297,728,000,000 | [
[
"Abbasi-Yadkori",
"Yasin",
""
],
[
"Pal",
"David",
""
],
[
"Szepesvari",
"Csaba",
""
]
] |
1102.2984 | Andreas Baldi | Rjab Hajlaoui, Mariem Gzara, Abdelaziz Dammak | Hybrid Model for Solving Multi-Objective Problems Using Evolutionary
Algorithm and Tabu Search | 5 pages | World of Computer Science and Information Technology Journal
(WCSIT),ISSN: 2221-0741,Vol. 1, No. 1, 5-9, Feb. 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new multi-objective hybrid model that makes cooperation
between the strength of research of neighborhood methods presented by the tabu
search (TS) and the important exploration capacity of evolutionary algorithm.
This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and
ZDT3), using a network of computers.
| [
{
"version": "v1",
"created": "Tue, 15 Feb 2011 07:43:03 GMT"
}
] | 1,297,814,400,000 | [
[
"Hajlaoui",
"Rjab",
""
],
[
"Gzara",
"Mariem",
""
],
[
"Dammak",
"Abdelaziz",
""
]
] |
1102.4924 | Minghao Yin | Junping Zhou, Minghao Yin | New Worst-Case Upper Bound for #XSAT | submitted to AAAI-10 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An algorithm running in O(1.1995n) is presented for counting models for exact
satisfiability formulae(#XSAT). This is faster than the previously best
algorithm which runs in O(1.2190n). In order to improve the efficiency of the
algorithm, a new principle, i.e. the common literals principle, is addressed to
simplify formulae. This allows us to eliminate more common literals. In
addition, we firstly inject the resolution principles into solving #XSAT
problem, and therefore this further improves the efficiency of the algorithm.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2011 08:16:59 GMT"
}
] | 1,298,592,000,000 | [
[
"Zhou",
"Junping",
""
],
[
"Yin",
"Minghao",
""
]
] |
1102.5385 | Martin Slota | Martin Slota and Jo\~ao Leite | Back and Forth Between Rules and SE-Models (Extended Version) | 25 pages; extended version of the paper accepted for LPNMR 2011 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rules in logic programming encode information about mutual interdependencies
between literals that is not captured by any of the commonly used semantics.
This information becomes essential as soon as a program needs to be modified or
further manipulated.
We argue that, in these cases, a program should not be viewed solely as the
set of its models. Instead, it should be viewed and manipulated as the set of
sets of models of each rule inside it. With this in mind, we investigate and
highlight relations between the SE-model semantics and individual rules. We
identify a set of representatives of rule equivalence classes induced by
SE-models, and so pinpoint the exact expressivity of this semantics with
respect to a single rule. We also characterise the class of sets of
SE-interpretations representable by a single rule. Finally, we discuss the
introduction of two notions of equivalence, both stronger than strong
equivalence [1] and weaker than strong update equivalence [2], which seem more
suitable whenever the dependency information found in rules is of interest.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2011 03:06:55 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Mar 2011 18:08:20 GMT"
}
] | 1,299,024,000,000 | [
[
"Slota",
"Martin",
""
],
[
"Leite",
"João",
""
]
] |
1102.5635 | Martin Josef Geiger | Martin Josef Geiger, Marc Sevaux | Practical inventory routing: A problem definition and an optimization
method | null | Proceedings of the EU/MEeting 2011 - Workshop on Client-Centered
Logistics and International Aid, February 21-22, 2011, pages 32-35 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The global objective of this work is to provide practical optimization
methods to companies involved in inventory routing problems, taking into
account this new type of data. Also, companies are sometimes not able to deal
with changing plans every period and would like to adopt regular structures for
serving customers.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2011 10:42:29 GMT"
}
] | 1,298,937,600,000 | [
[
"Geiger",
"Martin Josef",
""
],
[
"Sevaux",
"Marc",
""
]
] |
1103.0127 | Shobha Shankar | Shobha Shankar, Dr. T. Ananthapadmanabha | Fuzzy Approach to Critical Bus Ranking under Normal and Line Outage
Contingencies | 12 pages, 7 figures, CCSIT Conference | Advanced Computing, CCSIT Proceedings, Part III, pp. 400-406, Jan
2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identification of critical or weak buses for a given operating condition is
an important task in the load dispatch centre. It has become more vital in view
of the threat of voltage instability leading to voltage collapse. This paper
presents a fuzzy approach for ranking critical buses in a power system under
normal and network contingencies based on Line Flow index and voltage profiles
at load buses. The Line Flow index determines the maximum load that is possible
to be connected to a bus in order to maintain stability before the system
reaches its bifurcation point. Line Flow index (LF index) along with voltage
profiles at the load buses are represented in Fuzzy Set notation. Further they
are evaluated using fuzzy rules to compute Criticality Index. Based on this
index, critical buses are ranked. The bus with highest rank is the weakest bus
as it can withstand a small amount of load before causing voltage collapse. The
proposed method is tested on Five Bus Test System.
| [
{
"version": "v1",
"created": "Tue, 1 Mar 2011 10:35:44 GMT"
}
] | 1,299,024,000,000 | [
[
"Shankar",
"Shobha",
""
],
[
"Ananthapadmanabha",
"Dr. T.",
""
]
] |
1103.0632 | Hioual Ouassila | Hioual Ouassila and Boufaida Zizette | An Agent Based Architecture (Using Planning) for Dynamic and Semantic
Web Services Composition in an EBXML Context | 22 pages, 11 figures, 1 table | International Journal of Database Management Systems ( IJDMS ),
Vol.3, No.1, February 2011 110-131 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The process-based semantic composition of Web Services is gaining a
considerable momentum as an approach for the effective integration of
distributed, heterogeneous, and autonomous applications. To compose Web
Services semantically, we need an ontology. There are several ways of inserting
semantics in Web Services. One of them consists of using description languages
like OWL-S. In this paper, we introduce our work which consists in the
proposition of a new model and the use of semantic matching technology for
semantic and dynamic composition of ebXML business processes.
| [
{
"version": "v1",
"created": "Thu, 3 Mar 2011 09:44:06 GMT"
}
] | 1,433,116,800,000 | [
[
"Ouassila",
"Hioual",
""
],
[
"Zizette",
"Boufaida",
""
]
] |
1103.0697 | Adrian Walker | Adrian Walker | A Wiki for Business Rules in Open Vocabulary, Executable English | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of business-IT alignment is of widespread economic concern.
As one way of addressing the problem, this paper describes an online system
that functions as a kind of Wiki -- one that supports the collaborative writing
and running of business and scientific applications, as rules in open
vocabulary, executable English, using a browser.
Since the rules are in English, they are indexed by Google and other search
engines. This is useful when looking for rules for a task that one has in mind.
The design of the system integrates the semantics of data, with a semantics
of an inference method, and also with the meanings of English sentences. As
such, the system has functionality that may be useful for the Rules, Logic,
Proof and Trust requirements of the Semantic Web.
The system accepts rules, and small numbers of facts, typed or copy-pasted
directly into a browser. One can then run the rules, again using a browser. For
larger amounts of data, the system uses information in the rules to
automatically generate and run SQL over networked databases. From a few highly
declarative rules, the system typically generates SQL that would be too
complicated to write reliably by hand. However, the system can explain its
results in step-by-step hypertexted English, at the business or scientific
level
As befits a Wiki, shared use of the system is free.
| [
{
"version": "v1",
"created": "Thu, 3 Mar 2011 14:31:32 GMT"
}
] | 1,299,196,800,000 | [
[
"Walker",
"Adrian",
""
]
] |
1103.1003 | Eray Ozkural | Eray \"Ozkural | Teraflop-scale Incremental Machine Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a long-term memory design for artificial general intelligence
based on Solomonoff's incremental machine learning methods. We use R5RS Scheme
and its standard library with a few omissions as the reference machine. We
introduce a Levin Search variant based on Stochastic Context Free Grammar
together with four synergistic update algorithms that use the same grammar as a
guiding probability distribution of programs. The update algorithms include
adjusting production probabilities, re-using previous solutions, learning
programming idioms and discovery of frequent subprograms. Experiments with two
training sequences demonstrate that our approach to incremental learning is
effective.
| [
{
"version": "v1",
"created": "Sat, 5 Mar 2011 03:41:30 GMT"
}
] | 1,426,723,200,000 | [
[
"Özkural",
"Eray",
""
]
] |
1103.1157 | Nicola Di Mauro | Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, Floriana
Esposito | GRASP and path-relinking for Coalition Structure Generation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Artificial Intelligence with Coalition Structure Generation (CSG) one
refers to those cooperative complex problems that require to find an optimal
partition, maximising a social welfare, of a set of entities involved in a
system into exhaustive and disjoint coalitions. The solution of the CSG problem
finds applications in many fields such as Machine Learning (covering machines,
clustering), Data Mining (decision tree, discretization), Graph Theory, Natural
Language Processing (aggregation), Semantic Web (service composition), and
Bioinformatics. The problem of finding the optimal coalition structure is
NP-complete. In this paper we present a greedy adaptive search procedure
(GRASP) with path-relinking to efficiently search the space of coalition
structures. Experiments and comparisons to other algorithms prove the validity
of the proposed method in solving this hard combinatorial problem.
| [
{
"version": "v1",
"created": "Sun, 6 Mar 2011 18:54:04 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2011 10:55:28 GMT"
}
] | 1,299,715,200,000 | [
[
"Di Mauro",
"Nicola",
""
],
[
"Basile",
"Teresa M. A.",
""
],
[
"Ferilli",
"Stefano",
""
],
[
"Esposito",
"Floriana",
""
]
] |
1103.1205 | Minal Tomar | Minal Tomar and Pratibha Singh | A Directional Feature with Energy based Offline Signature Verification
Network | 10 pages, 6 figures | International Journal on Soft Computing ( IJSC ), Vol.2, No.1,
February 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Signature used as a biometric is implemented in various systems as well as
every signature signed by each person is distinct at the same time. So, it is
very important to have a computerized signature verification system. In offline
signature verification system dynamic features are not available obviously, but
one can use a signature as an image and apply image processing techniques to
make an effective offline signature verification system. Author proposes a
intelligent network used directional feature and energy density both as inputs
to the same network and classifies the signature. Neural network is used as a
classifier for this system. The results are compared with both the very basic
energy density method and a simple directional feature method of offline
signature verification system and this proposed new network is found very
effective as compared to the above two methods, specially for less number of
training samples, which can be implemented practically.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2011 07:17:13 GMT"
}
] | 1,299,542,400,000 | [
[
"Tomar",
"Minal",
""
],
[
"Singh",
"Pratibha",
""
]
] |
1103.1711 | D. Bryce | D. Bryce, S. Kambhampati, D. E. Smith | Planning Graph Heuristics for Belief Space Search | null | Journal Of Artificial Intelligence Research, Volume 26, pages
35-99, 2006 | 10.1613/jair.1869 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional planning.
| [
{
"version": "v1",
"created": "Wed, 9 Mar 2011 06:43:55 GMT"
}
] | 1,305,244,800,000 | [
[
"Bryce",
"D.",
""
],
[
"Kambhampati",
"S.",
""
],
[
"Smith",
"D. E.",
""
]
] |
1103.2091 | Tejbanta Singh Chingtham Mr | Tejbanta Singh Chingtham, G. Sahoo and M.K. Ghose | An Artificial Immune System Model for Multi-Agents Resource Sharing in
Distributed Environments | null | International Journal on Computer Science and Engineering (IJCSE),
Vol. 02, No. 05, 2010, pp 1813-1818 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural Immune system plays a vital role in the survival of the all living
being. It provides a mechanism to defend itself from external predates making
it consistent systems, capable of adapting itself for survival incase of
changes. The human immune system has motivated scientists and engineers for
finding powerful information processing algorithms that has solved complex
engineering tasks. This paper explores one of the various possibilities for
solving problem in a Multiagent scenario wherein multiple robots are deployed
to achieve a goal collectively. The final goal is dependent on the performance
of individual robot and its survival without having to lose its energy beyond a
predetermined threshold value by deploying an evolutionary computational
technique otherwise called the artificial immune system that imitates the
biological immune system.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2011 09:12:17 GMT"
}
] | 1,299,801,600,000 | [
[
"Chingtham",
"Tejbanta Singh",
""
],
[
"Sahoo",
"G.",
""
],
[
"Ghose",
"M. K.",
""
]
] |
1103.2342 | Tiago Silva | Tiago Silva and In\^es Dutra | SPPAM - Statistical PreProcessing AlgorithM | Submited to IJCAI11 conference on January 25, 2011 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most machine learning tools work with a single table where each row is an
instance and each column is an attribute. Each cell of the table contains an
attribute value for an instance. This representation prevents one important
form of learning, which is, classification based on groups of correlated
records, such as multiple exams of a single patient, internet customer
preferences, weather forecast or prediction of sea conditions for a given day.
To some extent, relational learning methods, such as inductive logic
programming, can capture this correlation through the use of intensional
predicates added to the background knowledge. In this work, we propose SPPAM,
an algorithm that aggregates past observations in one single record. We show
that applying SPPAM to the original correlated data, before the learning task,
can produce classifiers that are better than the ones trained using all
records.
| [
{
"version": "v1",
"created": "Fri, 11 Mar 2011 18:58:40 GMT"
}
] | 1,300,060,800,000 | [
[
"Silva",
"Tiago",
""
],
[
"Dutra",
"Inês",
""
]
] |
1103.2376 | Leonid Perlovsky | Leonid Perlovsky (Harvard University and the AFRL) | Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis | 16p, 2 figs | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An emotional version of Sapir-Whorf hypothesis suggests that differences in
language emotionalities influence differences among cultures no less than
conceptual differences. Conceptual contents of languages and cultures to
significant extent are determined by words and their semantic differences;
these could be borrowed among languages and exchanged among cultures. Emotional
differences, as suggested in the paper, are related to grammar and mostly
cannot be borrowed. Conceptual and emotional mechanisms of languages are
considered here along with their functions in the mind and cultural evolution.
A fundamental contradiction in human mind is considered: language evolution
requires reduced emotionality, but "too low" emotionality makes language
"irrelevant to life," disconnected from sensory-motor experience. Neural
mechanisms of these processes are suggested as well as their mathematical
models: the knowledge instinct, the language instinct, the dual model
connecting language and cognition, dynamic logic, neural modeling fields.
Mathematical results are related to cognitive science, linguistics, and
psychology. Experimental evidence and theoretical arguments are discussed.
Approximate equations for evolution of human minds and cultures are obtained.
Their solutions identify three types of cultures: "conceptual"-pragmatic
cultures, in which emotionality of language is reduced and differentiation
overtakes synthesis resulting in fast evolution at the price of uncertainty of
values, self doubts, and internal crises; "traditional-emotional" cultures
where differentiation lags behind synthesis, resulting in cultural stability at
the price of stagnation; and "multi-cultural" societies combining fast cultural
evolution and stability. Unsolved problems and future theoretical and
experimental directions are discussed.
| [
{
"version": "v1",
"created": "Fri, 11 Mar 2011 21:13:38 GMT"
}
] | 1,300,147,200,000 | [
[
"Perlovsky",
"Leonid",
"",
"Harvard University and the AFRL"
]
] |
1103.3123 | Yong Lai | Yong Lai, Dayou Liu, Shengsheng Wang | Reduced Ordered Binary Decision Diagram with Implied Literals: A New
knowledge Compilation Approach | 18 pages, 13 figures | null | 10.1007/s10115-012-0525-6 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge compilation is an approach to tackle the computational
intractability of general reasoning problems. According to this approach,
knowledge bases are converted off-line into a target compilation language which
is tractable for on-line querying. Reduced ordered binary decision diagram
(ROBDD) is one of the most influential target languages. We generalize ROBDD by
associating some implied literals in each node and the new language is called
reduced ordered binary decision diagram with implied literals (ROBDD-L). Then
we discuss a kind of subsets of ROBDD-L called ROBDD-i with precisely i implied
literals (0 \leq i \leq \infty). In particular, ROBDD-0 is isomorphic to ROBDD;
ROBDD-\infty requires that each node should be associated by the implied
literals as many as possible. We show that ROBDD-i has uniqueness over some
specific variables order, and ROBDD-\infty is the most succinct subset in
ROBDD-L and can meet most of the querying requirements involved in the
knowledge compilation map. Finally, we propose an ROBDD-i compilation algorithm
for any i and a ROBDD-\infty compilation algorithm. Based on them, we implement
a ROBDD-L package called BDDjLu and then get some conclusions from preliminary
experimental results: ROBDD-\infty is obviously smaller than ROBDD for all
benchmarks; ROBDD-\infty is smaller than the d-DNNF the benchmarks whose
compilation results are relatively small; it seems that it is better to
transform ROBDDs-\infty into FBDDs and ROBDDs rather than straight compile the
benchmarks.
| [
{
"version": "v1",
"created": "Wed, 16 Mar 2011 08:12:05 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Mar 2011 04:23:05 GMT"
}
] | 1,368,489,600,000 | [
[
"Lai",
"Yong",
""
],
[
"Liu",
"Dayou",
""
],
[
"Wang",
"Shengsheng",
""
]
] |
1103.3223 | Piero Giacomelli | Piero Giacomelli, Giulia Munaro and Roberto Rosso | Using Soft Computer Techniques on Smart Devices for Monitoring Chronic
Diseases: the CHRONIOUS case | presented at "The Third International Conference on eHealth,
Telemedicine, and Social Medicine (eTELEMED 2011)" | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | CHRONIOUS is an Open, Ubiquitous and Adaptive Chronic Disease Management
Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease
(CKD) and Renal Insufficiency. It consists of several modules: an ontology
based literature search engine, a rule based decision support system, remote
sensors interacting with lifestyle interfaces (PDA, monitor touchscreen) and a
machine learning module. All these modules interact each other to allow the
monitoring of two types of chronic diseases and to help clinician in taking
decision for cure purpose. This paper illustrates how some machine learning
algorithms and a rule based decision support system can be used in smart
devices, to monitor chronic patient. We will analyse how a set of machine
learning algorithms can be used in smart devices to alert the clinician in case
of a patient health condition worsening trend.
| [
{
"version": "v1",
"created": "Wed, 16 Mar 2011 16:28:00 GMT"
}
] | 1,300,320,000,000 | [
[
"Giacomelli",
"Piero",
""
],
[
"Munaro",
"Giulia",
""
],
[
"Rosso",
"Roberto",
""
]
] |
1103.3240 | Ken Duffy | K. R. Duffy and C. Bordenave and D. J. Leith | Decentralized Constraint Satisfaction | null | IEEE/ACM Transactions on Networking, 21 (4), 1298-1308, 2013 | 10.1109/TNET.2012.2222923 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that several important resource allocation problems in wireless
networks fit within the common framework of Constraint Satisfaction Problems
(CSPs). Inspired by the requirements of these applications, where variables are
located at distinct network devices that may not be able to communicate but may
interfere, we define natural criteria that a CSP solver must possess in order
to be practical. We term these algorithms decentralized CSP solvers. The best
known CSP solvers were designed for centralized problems and do not meet these
criteria. We introduce a stochastic decentralized CSP solver and prove that it
will find a solution in almost surely finite time, should one exist, also
showing it has many practically desirable properties. We benchmark the
algorithm's performance on a well-studied class of CSPs, random k-SAT,
illustrating that the time the algorithm takes to find a satisfying assignment
is competitive with stochastic centralized solvers on problems with order a
thousand variables despite its decentralized nature. We demonstrate the
solver's practical utility for the problems that motivated its introduction by
using it to find a non-interfering channel allocation for a network formed from
data from downtown Manhattan.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2011 15:00:09 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2011 14:44:16 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Sep 2011 11:00:47 GMT"
},
{
"version": "v4",
"created": "Tue, 9 Oct 2012 07:46:22 GMT"
}
] | 1,379,548,800,000 | [
[
"Duffy",
"K. R.",
""
],
[
"Bordenave",
"C.",
""
],
[
"Leith",
"D. J.",
""
]
] |
1103.3417 | Andreas Baldi | Hazim A. Farhan, Hussein H. Owaied, Suhaib I. Al-Ghazi | Finding Shortest Path for Developed Cognitive Map Using Medial Axis | 9 pages | World of Computer Science and Information Technology Journal
(WCSIT), ISSN: 2221-0741, Vol. 1, No. 2, 17-25, 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | this paper presents an enhancement of the medial axis algorithm to be used
for finding the optimal shortest path for developed cognitive map. The
cognitive map has been developed, based on the architectural blueprint maps.
The idea for using the medial-axis is to find main path central pixels; each
center pixel represents the center distance between two side boarder pixels.
The need for these pixels in the algorithm comes from the need of building a
network of nodes for the path, where each node represents a turning in the real
world (left, right, critical left, critical right...). The algorithm also
ignores from finding the center pixels paths that are too small for intelligent
robot navigation. The Idea of this algorithm is to find the possible shortest
path between start and end points. The goal of this research is to extract a
simple, robust representation of the shape of the cognitive map together with
the optimal shortest path between start and end points. The intelligent robot
will use this algorithm in order to decrease the time that is needed for
sweeping the targeted building.
| [
{
"version": "v1",
"created": "Thu, 17 Mar 2011 14:02:50 GMT"
}
] | 1,300,406,400,000 | [
[
"Farhan",
"Hazim A.",
""
],
[
"Owaied",
"Hussein H.",
""
],
[
"Al-Ghazi",
"Suhaib I.",
""
]
] |
1103.3420 | Sofiene Haboubi | Sofiene Haboubi and Samia Maddouri | Extraction of handwritten areas from colored image of bank checks by an
hybrid method | International Conference on Machine Intelligence (ACIDCA-ICIM),
Tozeur, Tunisia, November 2005 | null | null | null | cs.AI | http://creativecommons.org/licenses/publicdomain/ | One of the first step in the realization of an automatic system of check
recognition is the extraction of the handwritten area. We propose in this paper
an hybrid method to extract these areas. This method is based on digit
recognition by Fourier descriptors and different steps of colored image
processing . It requires the bank recognition of its code which is located in
the check marking band as well as the handwritten color recognition by the
method of difference of histograms. The areas extraction is then carried out by
the use of some mathematical morphology tools.
| [
{
"version": "v1",
"created": "Thu, 17 Mar 2011 14:13:36 GMT"
}
] | 1,300,406,400,000 | [
[
"Haboubi",
"Sofiene",
""
],
[
"Maddouri",
"Samia",
""
]
] |
1103.3687 | Subbarao Kambhampati | William Cushing and J. Benton and Subbarao Kambhampati | Cost Based Satisficing Search Considered Harmful | Longer version of an extended abstract from SOCS 2010 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, several researchers have found that cost-based satisficing search
with A* often runs into problems. Although some "work arounds" have been
proposed to ameliorate the problem, there has not been any concerted effort to
pinpoint its origin. In this paper, we argue that the origins can be traced
back to the wide variance in action costs that is observed in most planning
domains. We show that such cost variance misleads A* search, and that this is
no trifling detail or accidental phenomenon, but a systemic weakness of the
very concept of "cost-based evaluation functions + systematic search +
combinatorial graphs". We show that satisficing search with sized-based
evaluation functions is largely immune to this problem.
| [
{
"version": "v1",
"created": "Fri, 18 Mar 2011 18:57:46 GMT"
}
] | 1,300,665,600,000 | [
[
"Cushing",
"William",
""
],
[
"Benton",
"J.",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1103.3745 | Nina Narodytska | Christian Bessiere, Nina Narodytska, Claude-Guy Quimper, Toby Walsh | The AllDifferent Constraint with Precedences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose AllDiffPrecedence, a new global constraint that combines together
an AllDifferent constraint with precedence constraints that strictly order
given pairs of variables. We identify a number of applications for this global
constraint including instruction scheduling and symmetry breaking. We give an
efficient propagation algorithm that enforces bounds consistency on this global
constraint. We show how to implement this propagator using a decomposition that
extends the bounds consistency enforcing decomposition proposed for the
AllDifferent constraint. Finally, we prove that enforcing domain consistency on
this global constraint is NP-hard in general.
| [
{
"version": "v1",
"created": "Sat, 19 Mar 2011 03:50:45 GMT"
}
] | 1,300,752,000,000 | [
[
"Bessiere",
"Christian",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Quimper",
"Claude-Guy",
""
],
[
"Walsh",
"Toby",
""
]
] |
1103.3949 | Ana Sofia Gomes | Ana Sofia Gomes, Jose Julio Alferes, Terrance Swift | A Goal-Directed Implementation of Query Answering for Hybrid MKNF
Knowledge Bases | null | Theory and Practice of Logic Programming 14 (2014) 239-264 | 10.1017/S1471068412000439 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontologies and rules are usually loosely coupled in knowledge representation
formalisms. In fact, ontologies use open-world reasoning while the leading
semantics for rules use non-monotonic, closed-world reasoning. One exception is
the tightly-coupled framework of Minimal Knowledge and Negation as Failure
(MKNF), which allows statements about individuals to be jointly derived via
entailment from an ontology and inferences from rules. Nonetheless, the
practical usefulness of MKNF has not always been clear, although recent work
has formalized a general resolution-based method for querying MKNF when rules
are taken to have the well-founded semantics, and the ontology is modeled by a
general oracle. That work leaves open what algorithms should be used to relate
the entailments of the ontology and the inferences of rules. In this paper we
provide such algorithms, and describe the implementation of a query-driven
system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic)
rules under the well-founded semantics and a (monotonic) ontology, represented
by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic
Programming (TPLP)
| [
{
"version": "v1",
"created": "Mon, 21 Mar 2011 09:51:36 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Nov 2012 14:03:07 GMT"
}
] | 1,582,070,400,000 | [
[
"Gomes",
"Ana Sofia",
""
],
[
"Alferes",
"Jose Julio",
""
],
[
"Swift",
"Terrance",
""
]
] |
1103.3954 | Olivier Bailleux | Olivier Bailleux | BoolVar/PB v1.0, a java library for translating pseudo-Boolean
constraints into CNF formulae | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BoolVar/PB is an open source java library dedicated to the translation of
pseudo-Boolean constraints into CNF formulae. Input constraints can be
categorized with tags. Several encoding schemes are implemented in a way that
each input constraint can be translated using one or several encoders,
according to the related tags. The library can be easily extended by adding new
encoders and / or new output formats.
| [
{
"version": "v1",
"created": "Mon, 21 Mar 2011 10:14:40 GMT"
}
] | 1,300,752,000,000 | [
[
"Bailleux",
"Olivier",
""
]
] |
1103.5034 | Tong Chern | Tong Chern | On Understanding and Machine Understanding | due to some serious errors on page 2,3 and 5 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the present paper, we try to propose a self-similar network theory for the
basic understanding. By extending the natural languages to a kind of so called
idealy sufficient language, we can proceed a few steps to the investigation of
the language searching and the language understanding of AI.
Image understanding, and the familiarity of the brain to the surrounding
environment are also discussed. Group effects are discussed by addressing the
essense of the power of influences, and constructing the influence network of a
society. We also give a discussion of inspirations.
| [
{
"version": "v1",
"created": "Thu, 24 Mar 2011 03:35:24 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Feb 2018 14:02:38 GMT"
}
] | 1,517,529,600,000 | [
[
"Chern",
"Tong",
""
]
] |
1104.0843 | Minghao Yin | Jian Gao, Minghao Yin, and Ke Xu | Phase Transitions in Knowledge Compilation: an Experimental Study | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phase transitions in many complex combinational problems have been widely
studied in the past decade. In this paper, we investigate phase transitions in
the knowledge compilation empirically, where DFA, OBDD and d-DNNF are chosen as
the target languages to compile random k-SAT instances. We perform intensive
experiments to analyze the sizes of compilation results and draw the following
conclusions: there exists an easy-hard-easy pattern in compilations; the peak
point of sizes in the pattern is only related to the ratio of the number of
clauses to that of variables when k is fixed, regardless of target languages;
most sizes of compilation results increase exponentially with the number of
variables growing, but there also exists a phase transition that separates a
polynomial-increment region from the exponential-increment region; Moreover, we
explain why the phase transition in compilations occurs by analyzing
microstructures of DFAs, and conclude that a kind of solution
interchangeability with more than 2 variables has a sharp transition near the
peak point of the easy-hard-easy pattern, and thus it has a great impact on
sizes of DFAs.
| [
{
"version": "v1",
"created": "Tue, 5 Apr 2011 13:25:43 GMT"
},
{
"version": "v2",
"created": "Sun, 17 Apr 2011 12:41:23 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Jun 2011 07:05:11 GMT"
}
] | 1,307,318,400,000 | [
[
"Gao",
"Jian",
""
],
[
"Yin",
"Minghao",
""
],
[
"Xu",
"Ke",
""
]
] |
1104.1677 | Muhammad Zaheer Aslam | Bashir Ahmad, Shakeel Ahmad, Shahid Hussain, Muhammad Zaheer Aslam and
Zafar Abbas | Automatic Vehicle Checking Agent (VCA) | 5 pages, 2 figures | Control Theory and Informatics,ISSN 2224-5774 (print) ISSN
2225-0492 (online),Vol 1, No.2, 2011 | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | A definition of intelligence is given in terms of performance that can be
quantitatively measured. In this study, we have presented a conceptual model of
Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve
this goal, we have introduced several kinds of agents that exhibit intelligent
features. These are the Management agent, internal agent, External Agent,
Watcher agent and Report agent. Metrics and measurements are suggested for
evaluating the performance of Automatic Vehicle Checking Agent (VCA). Calibrate
data and test facilities are suggested to facilitate the development of
intelligent systems.
| [
{
"version": "v1",
"created": "Sat, 9 Apr 2011 06:31:24 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Dec 2011 17:22:50 GMT"
}
] | 1,323,129,600,000 | [
[
"Ahmad",
"Bashir",
""
],
[
"Ahmad",
"Shakeel",
""
],
[
"Hussain",
"Shahid",
""
],
[
"Aslam",
"Muhammad Zaheer",
""
],
[
"Abbas",
"Zafar",
""
]
] |
1104.1678 | Muhammad Zaheer Aslam | Muhammad Zaheer Aslam, Nasimullah, Abdur Rashid Khan | A Proposed Decision Support System/Expert System for Guiding Fresh
Students in Selecting a Faculty in Gomal University, Pakistan | I have withdrawn for some changes | Industrial Engineering Letters www.iiste.org ISSN 2224-6096
(Print) ISSN 2225-0581(Online) Vol 1, No.4, 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the design and development of a proposed rule based
Decision Support System that will help students in selecting the best suitable
faculty/major decision while taking admission in Gomal University, Dera Ismail
Khan, Pakistan. The basic idea of our approach is to design a model for testing
and measuring the student capabilities like intelligence, understanding,
comprehension, mathematical concepts plus his/her past academic record plus
his/her intelligence level, and applying the module results to a rule-based
decision support system to determine the compatibility of those capabilities
with the available faculties/majors in Gomal University. The result is shown as
a list of suggested faculties/majors with the student capabilities and
abilities.
| [
{
"version": "v1",
"created": "Sat, 9 Apr 2011 06:32:13 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Jan 2012 06:55:33 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Mar 2012 04:18:26 GMT"
}
] | 1,331,251,200,000 | [
[
"Aslam",
"Muhammad Zaheer",
""
],
[
"Nasimullah",
"",
""
],
[
"Khan",
"Abdur Rashid",
""
]
] |
1104.1924 | David Tolpin | David Tolpin, Solomon Eyal Shimony | Rational Deployment of CSP Heuristics | 7 pages, 2 figures, to appear in IJCAI-2011, http://www.ijcai.org/ | IJCAI-2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heuristics are crucial tools in decreasing search effort in varied fields of
AI. In order to be effective, a heuristic must be efficient to compute, as well
as provide useful information to the search algorithm. However, some well-known
heuristics which do well in reducing backtracking are so heavy that the gain of
deploying them in a search algorithm might be outweighed by their overhead.
We propose a rational metareasoning approach to decide when to deploy
heuristics, using CSP backtracking search as a case study. In particular, a
value of information approach is taken to adaptive deployment of solution-count
estimation heuristics for value ordering. Empirical results show that indeed
the proposed mechanism successfully balances the tradeoff between decreasing
backtracking and heuristic computational overhead, resulting in a significant
overall search time reduction.
| [
{
"version": "v1",
"created": "Mon, 11 Apr 2011 12:12:14 GMT"
}
] | 1,302,566,400,000 | [
[
"Tolpin",
"David",
""
],
[
"Shimony",
"Solomon Eyal",
""
]
] |
1104.3250 | Salah Rifai | Salah Rifai, Xavier Glorot, Yoshua Bengio, Pascal Vincent | Adding noise to the input of a model trained with a regularized
objective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regularization is a well studied problem in the context of neural networks.
It is usually used to improve the generalization performance when the number of
input samples is relatively small or heavily contaminated with noise. The
regularization of a parametric model can be achieved in different manners some
of which are early stopping (Morgan and Bourlard, 1990), weight decay, output
smoothing that are used to avoid overfitting during the training of the
considered model. From a Bayesian point of view, many regularization techniques
correspond to imposing certain prior distributions on model parameters (Krogh
and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective
function when a restricted type of noise is added to the input of a parametric
function, we derive the higher order terms of the Taylor expansion and analyze
the coefficients of the regularization terms induced by the noisy input. In
particular we study the effect of penalizing the Hessian of the mapping
function with respect to the input in terms of generalization performance. We
also show how we can control independently this coefficient by explicitly
penalizing the Jacobian of the mapping function on corrupted inputs.
| [
{
"version": "v1",
"created": "Sat, 16 Apr 2011 18:09:13 GMT"
}
] | 1,303,171,200,000 | [
[
"Rifai",
"Salah",
""
],
[
"Glorot",
"Xavier",
""
],
[
"Bengio",
"Yoshua",
""
],
[
"Vincent",
"Pascal",
""
]
] |
1104.3927 | Christian Drescher | Christian Drescher and Toby Walsh | Translation-based Constraint Answer Set Solving | Self-archived version for IJCAI'11 Best Paper Track submission | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We solve constraint satisfaction problems through translation to answer set
programming (ASP). Our reformulations have the property that unit-propagation
in the ASP solver achieves well defined local consistency properties like arc,
bound and range consistency. Experiments demonstrate the computational value of
this approach.
| [
{
"version": "v1",
"created": "Wed, 20 Apr 2011 02:31:07 GMT"
}
] | 1,303,344,000,000 | [
[
"Drescher",
"Christian",
""
],
[
"Walsh",
"Toby",
""
]
] |
1104.4053 | Maurizio Lenzerini | Maurizio Lenzerini, Domenico Fabio Savo | On the evolution of the instance level of DL-lite knowledge bases | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent papers address the issue of updating the instance level of knowledge
bases expressed in Description Logic following a model-based approach. One of
the outcomes of these papers is that the result of updating a knowledge base K
is generally not expressible in the Description Logic used to express K. In
this paper we introduce a formula-based approach to this problem, by revisiting
some research work on formula-based updates developed in the '80s, in
particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our
operator enjoys desirable properties, including that both insertions and
deletions according to such operator can be expressed in the DL used for the
original KB. Also, we present polynomial time algorithms for the evolution of
the instance level knowledge bases expressed in the most expressive Description
Logics of the DL-lite family.
| [
{
"version": "v1",
"created": "Wed, 20 Apr 2011 15:19:14 GMT"
}
] | 1,303,344,000,000 | [
[
"Lenzerini",
"Maurizio",
""
],
[
"Savo",
"Domenico Fabio",
""
]
] |
1104.4153 | Salah Rifai | Salah Rifai, Xavier Muller, Xavier Glorot, Gregoire Mesnil, Yoshua
Bengio and Pascal Vincent | Learning invariant features through local space contraction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present in this paper a novel approach for training deterministic
auto-encoders. We show that by adding a well chosen penalty term to the
classical reconstruction cost function, we can achieve results that equal or
surpass those attained by other regularized auto-encoders as well as denoising
auto-encoders on a range of datasets. This penalty term corresponds to the
Frobenius norm of the Jacobian matrix of the encoder activations with respect
to the input. We show that this penalty term results in a localized space
contraction which in turn yields robust features on the activation layer.
Furthermore, we show how this penalty term is related to both regularized
auto-encoders and denoising encoders and how it can be seen as a link between
deterministic and non-deterministic auto-encoders. We find empirically that
this penalty helps to carve a representation that better captures the local
directions of variation dictated by the data, corresponding to a
lower-dimensional non-linear manifold, while being more invariant to the vast
majority of directions orthogonal to the manifold. Finally, we show that by
using the learned features to initialize a MLP, we achieve state of the art
classification error on a range of datasets, surpassing other methods of
pre-training.
| [
{
"version": "v1",
"created": "Thu, 21 Apr 2011 01:39:25 GMT"
}
] | 1,303,430,400,000 | [
[
"Rifai",
"Salah",
""
],
[
"Muller",
"Xavier",
""
],
[
"Glorot",
"Xavier",
""
],
[
"Mesnil",
"Gregoire",
""
],
[
"Bengio",
"Yoshua",
""
],
[
"Vincent",
"Pascal",
""
]
] |
1104.4290 | Sebastian Ordyniak | Eun Jung Kim, Sebastian Ordyniak, Stefan Szeider | Algorithms and Complexity Results for Persuasive Argumentation | null | Artificial Intelligence 175 (2011) pp. 1722-1736 | 10.1016/j.artint.2011.03.001 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of arguments as abstract entities and their interaction as
introduced by Dung (Artificial Intelligence 177, 1995) has become one of the
most active research branches within Artificial Intelligence and Reasoning. A
main issue for abstract argumentation systems is the selection of acceptable
sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J.
Logic Comput. 13, 2003), extends Dung's framework. It takes into account the
relative strength of arguments with respect to some ranking representing an
audience: an argument is subjectively accepted if it is accepted with respect
to some audience, it is objectively accepted if it is accepted with respect to
all audiences. Deciding whether an argument is subjectively or objectively
accepted, respectively, are computationally intractable problems. In fact, the
problems remain intractable under structural restrictions that render the main
computational problems for non-value-based argumentation systems tractable. In
this paper we identify nontrivial classes of value-based argumentation systems
for which the acceptance problems are polynomial-time tractable. The classes
are defined by means of structural restrictions in terms of the underlying
graphical structure of the value-based system. Furthermore we show that the
acceptance problems are intractable for two classes of value-based systems that
where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007).
| [
{
"version": "v1",
"created": "Thu, 21 Apr 2011 15:22:36 GMT"
}
] | 1,305,504,000,000 | [
[
"Kim",
"Eun Jung",
""
],
[
"Ordyniak",
"Sebastian",
""
],
[
"Szeider",
"Stefan",
""
]
] |
1104.4910 | Minghao Yin | Jian Gao, Minghao Yin, Junping Zhou | Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction
Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the hybrid tractability of binary Quantified
Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of
binary QCSPs is identified by using the broken-triangle property. In this
class, the variable ordering for the broken-triangle property must be same as
that in the prefix of the QCSP. Second, we break this restriction to allow that
existentially quantified variables can be shifted within or out of their
blocks, and thus identify some novel tractable classes by introducing the
broken-angle property. Finally, we identify a more generalized tractable class,
i.e., the min-of-max extendable class for QCSPs.
| [
{
"version": "v1",
"created": "Tue, 26 Apr 2011 13:08:48 GMT"
}
] | 1,303,862,400,000 | [
[
"Gao",
"Jian",
""
],
[
"Yin",
"Minghao",
""
],
[
"Zhou",
"Junping",
""
]
] |
1104.5069 | Tuan Nguyen | Tuan Nguyen and Subbarao Kambhampati and Minh Do | Synthesizing Robust Plans under Incomplete Domain Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most current planners assume complete domain models and focus on generating
correct plans. Unfortunately, domain modeling is a laborious and error-prone
task. While domain experts cannot guarantee completeness, often they are able
to circumscribe the incompleteness of the model by providing annotations as to
which parts of the domain model may be incomplete. In such cases, the goal
should be to generate plans that are robust with respect to any known
incompleteness of the domain. In this paper, we first introduce annotations
expressing the knowledge of the domain incompleteness, and formalize the notion
of plan robustness with respect to an incomplete domain model. We then propose
an approach to compiling the problem of finding robust plans to the conformant
probabilistic planning problem. We present experimental results with
Probabilistic-FF, a state-of-the-art planner, showing the promise of our
approach.
| [
{
"version": "v1",
"created": "Wed, 27 Apr 2011 04:05:19 GMT"
}
] | 1,303,948,800,000 | [
[
"Nguyen",
"Tuan",
""
],
[
"Kambhampati",
"Subbarao",
""
],
[
"Do",
"Minh",
""
]
] |
1105.0288 | Martin Slota | Martin Slota and Jo\~ao Leite and Terrance Swift | Splitting and Updating Hybrid Knowledge Bases (Extended Version) | 64 pages; extended version of the paper accepted for ICLP 2011 | Theory and Practice of Logic Programming, 11(4-5), 801-819, 2011 | 10.1017/S1471068411000317 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the years, nonmonotonic rules have proven to be a very expressive and
useful knowledge representation paradigm. They have recently been used to
complement the expressive power of Description Logics (DLs), leading to the
study of integrative formal frameworks, generally referred to as hybrid
knowledge bases, where both DL axioms and rules can be used to represent
knowledge. The need to use these hybrid knowledge bases in dynamic domains has
called for the development of update operators, which, given the substantially
different way Description Logics and rules are usually updated, has turned out
to be an extremely difficult task.
In [SL10], a first step towards addressing this problem was taken, and an
update operator for hybrid knowledge bases was proposed. Despite its
significance -- not only for being the first update operator for hybrid
knowledge bases in the literature, but also because it has some applications -
this operator was defined for a restricted class of problems where only the
ABox was allowed to change, which considerably diminished its applicability.
Many applications that use hybrid knowledge bases in dynamic scenarios require
both DL axioms and rules to be updated.
In this paper, motivated by real world applications, we introduce an update
operator for a large class of hybrid knowledge bases where both the DL
component as well as the rule component are allowed to dynamically change. We
introduce splitting sequences and splitting theorem for hybrid knowledge bases,
use them to define a modular update semantics, investigate its basic
properties, and illustrate its use on a realistic example about cargo imports.
| [
{
"version": "v1",
"created": "Mon, 2 May 2011 10:12:59 GMT"
}
] | 1,311,724,800,000 | [
[
"Slota",
"Martin",
""
],
[
"Leite",
"João",
""
],
[
"Swift",
"Terrance",
""
]
] |
1105.0650 | Miroslaw Truszczynski | Yuliya Lierler and Miroslaw Truszczynski | Transition Systems for Model Generators - A Unifying Approach | 30 pages; Accepted for presentation at ICLP 2011 and for publication
in Theory and Practice of Logic Programming; contains the appendix with
proofs | Theory and Practice of Logic Programming, volume 11, issue 4-5, pp
629 - 646, 2011 | 10.1017/S1471068411000214 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fundamental task for propositional logic is to compute models of
propositional formulas. Programs developed for this task are called
satisfiability solvers. We show that transition systems introduced by
Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers
can be adapted for solvers developed for two other propositional formalisms:
logic programming under the answer-set semantics, and the logic PC(ID). We show
that in each case the task of computing models can be seen as "satisfiability
modulo answer-set programming," where the goal is to find a model of a theory
that also is an answer set of a certain program. The unifying perspective we
develop shows, in particular, that solvers CLASP and MINISATID are closely
related despite being developed for different formalisms, one for answer-set
programming and the latter for the logic PC(ID).
| [
{
"version": "v1",
"created": "Tue, 3 May 2011 18:29:58 GMT"
}
] | 1,412,726,400,000 | [
[
"Lierler",
"Yuliya",
""
],
[
"Truszczynski",
"Miroslaw",
""
]
] |
1105.0974 | Seyed Salim Tabatabaei | Seyed Salim Tabatabaei and Mark Coates and Michael Rabbat | GANC: Greedy Agglomerative Normalized Cut | Submitted to Pattern Recognition. 27 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a graph clustering algorithm that aims to minimize the
normalized cut criterion and has a model order selection procedure. The
performance of the proposed algorithm is comparable to spectral approaches in
terms of minimizing normalized cut. However, unlike spectral approaches, the
proposed algorithm scales to graphs with millions of nodes and edges. The
algorithm consists of three components that are processed sequentially: a
greedy agglomerative hierarchical clustering procedure, model order selection,
and a local refinement.
For a graph of n nodes and O(n) edges, the computational complexity of the
algorithm is O(n log^2 n), a major improvement over the O(n^3) complexity of
spectral methods. Experiments are performed on real and synthetic networks to
demonstrate the scalability of the proposed approach, the effectiveness of the
model order selection procedure, and the performance of the proposed algorithm
in terms of minimizing the normalized cut metric.
| [
{
"version": "v1",
"created": "Thu, 5 May 2011 04:55:53 GMT"
}
] | 1,304,640,000,000 | [
[
"Tabatabaei",
"Seyed Salim",
""
],
[
"Coates",
"Mark",
""
],
[
"Rabbat",
"Michael",
""
]
] |
1105.1247 | Manojit Chattopadhyay Mr. | Manojit Chattopadhyay (Pailan College of Management & Technology),
Surajit Chattopadhyay (Pailan College of Management & Technology), Pranab K.
Dan (West Bengal University of Technology) | Machine-Part cell formation through visual decipherable clustering of
Self Organizing Map | 18 pages,3 table, 4 figures | The International Journal of Advanced Manufacturing Technology,
2011, Volume 52, Numbers 9-12, 1019-1030 | 10.1007/s00170-010-2802-4 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.
| [
{
"version": "v1",
"created": "Fri, 6 May 2011 09:27:49 GMT"
}
] | 1,304,899,200,000 | [
[
"Chattopadhyay",
"Manojit",
"",
"Pailan College of Management & Technology"
],
[
"Chattopadhyay",
"Surajit",
"",
"Pailan College of Management & Technology"
],
[
"Dan",
"Pranab K.",
"",
"West Bengal University of Technology"
]
] |
1105.1436 | Jingchao Chen | Jingchao Chen | Solving Rubik's Cube Using SAT Solvers | 13 pages | SPA 2011: SAT for Practical Applications | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Rubik's Cube is an easily-understood puzzle, which is originally called the
"magic cube". It is a well-known planning problem, which has been studied for a
long time. Yet many simple properties remain unknown. This paper studies
whether modern SAT solvers are applicable to this puzzle. To our best
knowledge, we are the first to translate Rubik's Cube to a SAT problem. To
reduce the number of variables and clauses needed for the encoding, we replace
a naive approach of 6 Boolean variables to represent each color on each facelet
with a new approach of 3 or 2 Boolean variables. In order to be able to solve
quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer
encoding of 18-subtype turns based on 6-type turns. To speed up the solving
further, we encode some properties of two-phase algorithm as an additional
constraint, and restrict some move sequences by adding some constraint clauses.
Using only efficient encoding cannot solve this puzzle. For this reason, we
improve the existing SAT solvers, and develop a new SAT solver based on
PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver
replaces the lookahead solving strategy with an ALO (\emph{at-least-one})
solving strategy, and decomposes the original problem into sub-problems. Each
sub-problem is solved by PrecoSAT. The empirical results demonstrate both our
SAT translation and new solving technique are efficient. Without the efficient
SAT encoding and the new solving technique, Rubik's Cube will not be able to be
solved still by any SAT solver. Using the improved SAT solver, we can find
always a solution of length 20 in a reasonable time. Although our solver is
slower than Kociemba's algorithm using lookup tables, but does not require a
huge lookup table.
| [
{
"version": "v1",
"created": "Sat, 7 May 2011 12:07:49 GMT"
}
] | 1,304,985,600,000 | [
[
"Chen",
"Jingchao",
""
]
] |
1105.1929 | Fabian Suchanek | Fabian Suchanek (INRIA Saclay - Ile de France), Aparna Varde, Richi
Nayak (QUT), Pierre Senellart | The Hidden Web, XML and Semantic Web: A Scientific Data Management
Perspective | EDBT - Tutorial (2011) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The World Wide Web no longer consists just of HTML pages. Our work sheds
light on a number of trends on the Internet that go beyond simple Web pages.
The hidden Web provides a wealth of data in semi-structured form, accessible
through Web forms and Web services. These services, as well as numerous other
applications on the Web, commonly use XML, the eXtensible Markup Language. XML
has become the lingua franca of the Internet that allows customized markups to
be defined for specific domains. On top of XML, the Semantic Web grows as a
common structured data source. In this work, we first explain each of these
developments in detail. Using real-world examples from scientific domains of
great interest today, we then demonstrate how these new developments can assist
the managing, harvesting, and organization of data on the Web. On the way, we
also illustrate the current research avenues in these domains. We believe that
this effort would help bridge multiple database tracks, thereby attracting
researchers with a view to extend database technology.
| [
{
"version": "v1",
"created": "Tue, 10 May 2011 12:33:41 GMT"
}
] | 1,305,072,000,000 | [
[
"Suchanek",
"Fabian",
"",
"INRIA Saclay - Ile de France"
],
[
"Varde",
"Aparna",
"",
"QUT"
],
[
"Nayak",
"Richi",
"",
"QUT"
],
[
"Senellart",
"Pierre",
""
]
] |
1105.2902 | Zahra Forootan Jahromi | Zahra Forootan Jahromi, Amir Rajabzadeh and Ali Reza Manashty | A Multi-Purpose Scenario-based Simulator for Smart House Environments | null | (IJCSIS) International Journal of Computer Science and Information
Security, Vol. 9, No. 1, January 2011 | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | Developing smart house systems has been a great challenge for researchers and
engineers in this area because of the high cost of implementation and
evaluation process of these systems, while being very time consuming. Testing a
designed smart house before actually building it is considered as an obstacle
towards an efficient smart house project. This is because of the variety of
sensors, home appliances and devices available for a real smart environment. In
this paper, we present the design and implementation of a multi-purpose smart
house simulation system for designing and simulating all aspects of a smart
house environment. This simulator provides the ability to design the house plan
and different virtual sensors and appliances in a two dimensional model of the
virtual house environment. This simulator can connect to any external smart
house remote controlling system, providing evaluation capabilities to their
system much easier than before. It also supports detailed adding of new
emerging sensors and devices to help maintain its compatibility with future
simulation needs. Scenarios can also be defined for testing various possible
combinations of device states; so different criteria and variables can be
simply evaluated without the need of experimenting on a real environment.
| [
{
"version": "v1",
"created": "Sat, 14 May 2011 15:11:00 GMT"
}
] | 1,305,590,400,000 | [
[
"Jahromi",
"Zahra Forootan",
""
],
[
"Rajabzadeh",
"Amir",
""
],
[
"Manashty",
"Ali Reza",
""
]
] |
1105.3486 | Ladislau B\"ol\"oni | Ladislau B\"ol\"oni | Xapagy: a cognitive architecture for narrative reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the Xapagy cognitive architecture: a software system designed to
perform narrative reasoning. The architecture has been designed from scratch to
model and mimic the activities performed by humans when witnessing, reading,
recalling, narrating and talking about stories.
| [
{
"version": "v1",
"created": "Tue, 17 May 2011 20:28:31 GMT"
}
] | 1,426,723,200,000 | [
[
"Bölöni",
"Ladislau",
""
]
] |
1105.3635 | M. C. Garrido | M. C. Garrido, P. E. Lopez-de-Teruel, A. Ruiz | Probabilistic Inference from Arbitrary Uncertainty using Mixtures of
Factorized Generalized Gaussians | null | Journal Of Artificial Intelligence Research, Volume 9, pages
167-217, 1998 | 10.1613/jair.533 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a general and efficient framework for probabilistic
inference and learning from arbitrary uncertain information. It exploits the
calculation properties of finite mixture models, conjugate families and
factorization. Both the joint probability density of the variables and the
likelihood function of the (objective or subjective) observation are
approximated by a special mixture model, in such a way that any desired
conditional distribution can be directly obtained without numerical
integration. We have developed an extended version of the expectation
maximization (EM) algorithm to estimate the parameters of mixture models from
uncertain training examples (indirect observations). As a consequence, any
piece of exact or uncertain information about both input and output values is
consistently handled in the inference and learning stages. This ability,
extremely useful in certain situations, is not found in most alternative
methods. The proposed framework is formally justified from standard
probabilistic principles and illustrative examples are provided in the fields
of nonparametric pattern classification, nonlinear regression and pattern
completion. Finally, experiments on a real application and comparative results
over standard databases provide empirical evidence of the utility of the method
in a wide range of applications.
| [
{
"version": "v1",
"created": "Wed, 18 May 2011 14:06:49 GMT"
}
] | 1,305,763,200,000 | [
[
"Garrido",
"M. C.",
""
],
[
"Lopez-de-Teruel",
"P. E.",
""
],
[
"Ruiz",
"A.",
""
]
] |
1105.3821 | Peter de Blanc | Peter de Blanc | Ontological Crises in Artificial Agents' Value Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision-theoretic agents predict and evaluate the results of their actions
using a model, or ontology, of their environment. An agent's goal, or utility
function, may also be specified in terms of the states of, or entities within,
its ontology. If the agent may upgrade or replace its ontology, it faces a
crisis: the agent's original goal may not be well-defined with respect to its
new ontology. This crisis must be resolved before the agent can make plans
towards achieving its goals.
We discuss in this paper which sorts of agents will undergo ontological
crises and why we may want to create such agents. We present some concrete
examples, and argue that a well-defined procedure for resolving ontological
crises is needed. We point to some possible approaches to solving this problem,
and evaluate these methods on our examples.
| [
{
"version": "v1",
"created": "Thu, 19 May 2011 09:32:46 GMT"
}
] | 1,305,849,600,000 | [
[
"de Blanc",
"Peter",
""
]
] |
1105.3833 | Eliezer Lozinskii | Eliezer L. Lozinskii | Typical models: minimizing false beliefs | null | Journal of Experimental & Theoretical Artificial Intelligence,
vol. 22, no.4, December 2010, 321-340 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A knowledge system S describing a part of real world does in general not
contain complete information. Reasoning with incomplete information is prone to
errors since any belief derived from S may be false in the present state of the
world. A false belief may suggest wrong decisions and lead to harmful actions.
So an important goal is to make false beliefs as unlikely as possible. This
work introduces the notions of "typical atoms" and "typical models", and shows
that reasoning with typical models minimizes the expected number of false
beliefs over all ways of using incomplete information. Various properties of
typical models are studied, in particular, correctness and stability of beliefs
suggested by typical models, and their connection to oblivious reasoning.
| [
{
"version": "v1",
"created": "Thu, 19 May 2011 10:00:39 GMT"
}
] | 1,305,849,600,000 | [
[
"Lozinskii",
"Eliezer L.",
""
]
] |
1105.5440 | J. M. Ahuactzin | J. M. Ahuactzin, P. Bessiere, E. Mazer | The Ariadne's Clew Algorithm | null | Journal Of Artificial Intelligence Research, Volume 9, pages
295-316, 1998 | 10.1613/jair.468 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new approach to path planning, called the "Ariadne's clew
algorithm". It is designed to find paths in high-dimensional continuous spaces
and applies to robots with many degrees of freedom in static, as well as
dynamic environments - ones where obstacles may move. The Ariadne's clew
algorithm comprises two sub-algorithms, called Search and Explore, applied in
an interleaved manner. Explore builds a representation of the accessible space
while Search looks for the target. Both are posed as optimization problems. We
describe a real implementation of the algorithm to plan paths for a six degrees
of freedom arm in a dynamic environment where another six degrees of freedom
arm is used as a moving obstacle. Experimental results show that a path is
found in about one second without any pre-processing.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:44:34 GMT"
}
] | 1,306,713,600,000 | [
[
"Ahuactzin",
"J. M.",
""
],
[
"Bessiere",
"P.",
""
],
[
"Mazer",
"E.",
""
]
] |
1105.5441 | C. Backstrom | C. Backstrom | Computational Aspects of Reordering Plans | null | Journal Of Artificial Intelligence Research, Volume 9, pages
99-137, 1998 | 10.1613/jair.477 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article studies the problem of modifying the action ordering of a plan
in order to optimise the plan according to various criteria. One of these
criteria is to make a plan less constrained and the other is to minimize its
parallel execution time. Three candidate definitions are proposed for the first
of these criteria, constituting a sequence of increasing optimality guarantees.
Two of these are based on deordering plans, which means that ordering relations
may only be removed, not added, while the third one uses reordering, where
arbitrary modifications to the ordering are allowed. It is shown that only the
weakest one of the three criteria is tractable to achieve, the other two being
NP-hard and even difficult to approximate. Similarly, optimising the parallel
execution time of a plan is studied both for deordering and reordering of
plans. In the general case, both of these computations are NP-hard. However, it
is shown that optimal deorderings can be computed in polynomial time for a
class of planning languages based on the notions of producers, consumers and
threats, which includes most of the commonly used planning languages. Computing
optimal reorderings can potentially lead to even faster parallel executions,
but this problem remains NP-hard and difficult to approximate even under quite
severe restrictions.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:44:57 GMT"
}
] | 1,306,713,600,000 | [
[
"Backstrom",
"C.",
""
]
] |
1105.5442 | O. Ledeniov | O. Ledeniov, S. Markovitch | The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic
Inference | null | Journal Of Artificial Intelligence Research, Volume 9, pages
37-97, 1998 | 10.1613/jair.509 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is common to view programs as a combination of logic and control: the
logic part defines what the program must do, the control part -- how to do it.
The Logic Programming paradigm was developed with the intention of separating
the logic from the control. Recently, extensive research has been conducted on
automatic generation of control for logic programs. Only a few of these works
considered the issue of automatic generation of control for improving the
efficiency of logic programs. In this paper we present a novel algorithm for
automatic finding of lowest-cost subgoal orderings. The algorithm works using
the divide-and-conquer strategy. The given set of subgoals is partitioned into
smaller sets, based on co-occurrence of free variables. The subsets are ordered
recursively and merged, yielding a provably optimal order. We experimentally
demonstrate the utility of the algorithm by testing it in several domains, and
discuss the possibilities of its cooperation with other existing methods.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:45:23 GMT"
}
] | 1,306,713,600,000 | [
[
"Ledeniov",
"O.",
""
],
[
"Markovitch",
"S.",
""
]
] |
1105.5443 | J. Culberson | J. Culberson, B. Vandegriend | The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem | null | Journal Of Artificial Intelligence Research, Volume 9, pages
219-245, 1998 | 10.1613/jair.512 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using an improved backtrack algorithm with sophisticated pruning techniques,
we revise previous observations correlating a high frequency of hard to solve
Hamiltonian Cycle instances with the Gn,m phase transition between
Hamiltonicity and non-Hamiltonicity. Instead all tested graphs of 100 to 1500
vertices are easily solved. When we artificially restrict the degree sequence
with a bounded maximum degree, although there is some increase in difficulty,
the frequency of hard graphs is still low. When we consider more regular graphs
based on a generalization of knight's tours, we observe frequent instances of
really hard graphs, but on these the average degree is bounded by a constant.
We design a set of graphs with a feature our algorithm is unable to detect and
so are very hard for our algorithm, but in these we can vary the average degree
from O(1) to O(n). We have so far found no class of graphs correlated with the
Gn,m phase transition which asymptotically produces a high frequency of hard
instances.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:45:52 GMT"
}
] | 1,306,713,600,000 | [
[
"Culberson",
"J.",
""
],
[
"Vandegriend",
"B.",
""
]
] |
1105.5444 | P. Resnik | P. Resnik | Semantic Similarity in a Taxonomy: An Information-Based Measure and its
Application to Problems of Ambiguity in Natural Language | null | Journal Of Artificial Intelligence Research, Volume 11, pages
95-130, 1999 | 10.1613/jair.514 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents a measure of semantic similarity in an IS-A taxonomy
based on the notion of shared information content. Experimental evaluation
against a benchmark set of human similarity judgments demonstrates that the
measure performs better than the traditional edge-counting approach. The
article presents algorithms that take advantage of taxonomic similarity in
resolving syntactic and semantic ambiguity, along with experimental results
demonstrating their effectiveness.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:46:05 GMT"
}
] | 1,306,713,600,000 | [
[
"Resnik",
"P.",
""
]
] |
1105.5446 | A. Artale | A. Artale, E. Franconi | A Temporal Description Logic for Reasoning about Actions and Plans | null | Journal Of Artificial Intelligence Research, Volume 9, pages
463-506, 1998 | 10.1613/jair.516 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A class of interval-based temporal languages for uniformly representing and
reasoning about actions and plans is presented. Actions are represented by
describing what is true while the action itself is occurring, and plans are
constructed by temporally relating actions and world states. The temporal
languages are members of the family of Description Logics, which are
characterized by high expressivity combined with good computational properties.
The subsumption problem for a class of temporal Description Logics is
investigated and sound and complete decision procedures are given. The basic
language TL-F is considered first: it is the composition of a temporal logic TL
-- able to express interval temporal networks -- together with the non-temporal
logic F -- a Feature Description Logic. It is proven that subsumption in this
language is an NP-complete problem. Then it is shown how to reason with the
more expressive languages TLU-FU and TL-ALCF. The former adds disjunction both
at the temporal and non-temporal sides of the language, the latter extends the
non-temporal side with set-valued features (i.e., roles) and a propositionally
complete language.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:46:39 GMT"
}
] | 1,306,713,600,000 | [
[
"Artale",
"A.",
""
],
[
"Franconi",
"E.",
""
]
] |
1105.5447 | D. J. Cook | D. J. Cook, R. C. Varnell | Adaptive Parallel Iterative Deepening Search | null | Journal Of Artificial Intelligence Research, Volume 9, pages
139-165, 1998 | 10.1613/jair.518 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:47:18 GMT"
}
] | 1,306,713,600,000 | [
[
"Cook",
"D. J.",
""
],
[
"Varnell",
"R. C.",
""
]
] |
1105.5448 | E. Davis | E. Davis | Order of Magnitude Comparisons of Distance | null | Journal Of Artificial Intelligence Research, Volume 10, pages
1-38, 1999 | 10.1613/jair.520 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Order of magnitude reasoning - reasoning by rough comparisons of the sizes of
quantities - is often called 'back of the envelope calculation', with the
implication that the calculations are quick though approximate. This paper
exhibits an interesting class of constraint sets in which order of magnitude
reasoning is demonstrably fast. Specifically, we present a polynomial-time
algorithm that can solve a set of constraints of the form 'Points a and b are
much closer together than points c and d.' We prove that this algorithm can be
applied if `much closer together' is interpreted either as referring to an
infinite difference in scale or as referring to a finite difference in scale,
as long as the difference in scale is greater than the number of variables in
the constraint set. We also prove that the first-order theory over such
constraints is decidable.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:47:48 GMT"
}
] | 1,306,713,600,000 | [
[
"Davis",
"E.",
""
]
] |
1105.5449 | G. Di Caro | G. Di Caro, M. Dorigo | AntNet: Distributed Stigmergetic Control for Communications Networks | null | Journal Of Artificial Intelligence Research, Volume 9, pages
317-365, 1998 | 10.1613/jair.530 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces AntNet, a novel approach to the adaptive learning of
routing tables in communications networks. AntNet is a distributed, mobile
agents based Monte Carlo system that was inspired by recent work on the ant
colony metaphor for solving optimization problems. AntNet's agents concurrently
explore the network and exchange collected information. The communication among
the agents is indirect and asynchronous, mediated by the network itself. This
form of communication is typical of social insects and is called stigmergy. We
compare our algorithm with six state-of-the-art routing algorithms coming from
the telecommunications and machine learning fields. The algorithms' performance
is evaluated over a set of realistic testbeds. We run many experiments over
real and artificial IP datagram networks with increasing number of nodes and
under several paradigmatic spatial and temporal traffic distributions. Results
are very encouraging. AntNet showed superior performance under all the
experimental conditions with respect to its competitors. We analyze the main
characteristics of the algorithm and try to explain the reasons for its
superiority.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:48:39 GMT"
}
] | 1,306,713,600,000 | [
[
"Di Caro",
"G.",
""
],
[
"Dorigo",
"M.",
""
]
] |
1105.5450 | J. Y. Halpern | J. Y. Halpern | A Counter Example to Theorems of Cox and Fine | null | Journal Of Artificial Intelligence Research, Volume 10, pages
67-85, 1999 | 10.1613/jair.536 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cox's well-known theorem justifying the use of probability is shown not to
hold in finite domains. The counterexample also suggests that Cox's assumptions
are insufficient to prove the result even in infinite domains. The same
counterexample is used to disprove a result of Fine on comparative conditional
probability.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:49:04 GMT"
}
] | 1,306,713,600,000 | [
[
"Halpern",
"J. Y.",
""
]
] |
1105.5451 | M. Fox | M. Fox, D. Long | The Automatic Inference of State Invariants in TIM | null | Journal Of Artificial Intelligence Research, Volume 9, pages
367-421, 1998 | 10.1613/jair.544 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As planning is applied to larger and richer domains the effort involved in
constructing domain descriptions increases and becomes a significant burden on
the human application designer. If general planners are to be applied
successfully to large and complex domains it is necessary to provide the domain
designer with some assistance in building correctly encoded domains. One way of
doing this is to provide domain-independent techniques for extracting, from a
domain description, knowledge that is implicit in that description and that can
assist domain designers in debugging domain descriptions. This knowledge can
also be exploited to improve the performance of planners: several researchers
have explored the potential of state invariants in speeding up the performance
of domain-independent planners. In this paper we describe a process by which
state invariants can be extracted from the automatically inferred type
structure of a domain. These techniques are being developed for exploitation by
STAN, a Graphplan based planner that employs state analysis techniques to
enhance its performance.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:49:44 GMT"
}
] | 1,306,713,600,000 | [
[
"Fox",
"M.",
""
],
[
"Long",
"D.",
""
]
] |
1105.5452 | D. Calvanese | D. Calvanese, M. Lenzerini, D. Nardi | Unifying Class-Based Representation Formalisms | null | Journal Of Artificial Intelligence Research, Volume 11, pages
199-240, 1999 | 10.1613/jair.548 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notion of class is ubiquitous in computer science and is central in many
formalisms for the representation of structured knowledge used both in
knowledge representation and in databases. In this paper we study the basic
issues underlying such representation formalisms and single out both their
common characteristics and their distinguishing features. Such investigation
leads us to propose a unifying framework in which we are able to capture the
fundamental aspects of several representation languages used in different
contexts. The proposed formalism is expressed in the style of description
logics, which have been introduced in knowledge representation as a means to
provide a semantically well-founded basis for the structural aspects of
knowledge representation systems. The description logic considered in this
paper is a subset of first order logic with nice computational characteristics.
It is quite expressive and features a novel combination of constructs that has
not been studied before. The distinguishing constructs are number restrictions,
which generalize existence and functional dependencies, inverse roles, which
allow one to refer to the inverse of a relationship, and possibly cyclic
assertions, which are necessary for capturing real world domains. We are able
to show that it is precisely such combination of constructs that makes our
logic powerful enough to model the essential set of features for defining class
structures that are common to frame systems, object-oriented database
languages, and semantic data models. As a consequence of the established
correspondences, several significant extensions of each of the above formalisms
become available. The high expressiveness of the logic we propose and the need
for capturing the reasoning in different contexts forces us to distinguish
between unrestricted and finite model reasoning. A notable feature of our
proposal is that reasoning in both cases is decidable. We argue that, by virtue
of the high expressive power and of the associated reasoning capabilities on
both unrestricted and finite models, our logic provides a common core for
class-based representation formalisms.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:49:59 GMT"
}
] | 1,306,713,600,000 | [
[
"Calvanese",
"D.",
""
],
[
"Lenzerini",
"M.",
""
],
[
"Nardi",
"D.",
""
]
] |
1105.5453 | J. Rintanen | J. Rintanen | Complexity of Prioritized Default Logics | null | Journal Of Artificial Intelligence Research, Volume 9, pages
423-461, 1998 | 10.1613/jair.554 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In default reasoning, usually not all possible ways of resolving conflicts
between default rules are acceptable. Criteria expressing acceptable ways of
resolving the conflicts may be hardwired in the inference mechanism, for
example specificity in inheritance reasoning can be handled this way, or they
may be given abstractly as an ordering on the default rules. In this article we
investigate formalizations of the latter approach in Reiter's default logic.
Our goal is to analyze and compare the computational properties of three such
formalizations in terms of their computational complexity: the prioritized
default logics of Baader and Hollunder, and Brewka, and a prioritized default
logic that is based on lexicographic comparison. The analysis locates the
propositional variants of these logics on the second and third levels of the
polynomial hierarchy, and identifies the boundary between tractable and
intractable inference for restricted classes of prioritized default theories.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:50:12 GMT"
}
] | 1,306,713,600,000 | [
[
"Rintanen",
"J.",
""
]
] |
1105.5454 | D. P. Clements | D. P. Clements, D. E. Joslin | Squeaky Wheel Optimization | null | Journal Of Artificial Intelligence Research, Volume 10, pages
353-373, 1999 | 10.1613/jair.561 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a general approach to optimization which we term `Squeaky Wheel'
Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution
which is then analyzed to find the trouble spots, i.e., those elements, that,
if improved, are likely to improve the objective function score. The results of
the analysis are used to generate new priorities that determine the order in
which the greedy algorithm constructs the next solution. This
Construct/Analyze/Prioritize cycle continues until some limit is reached, or an
acceptable solution is found. SWO can be viewed as operating on two search
spaces: solutions and prioritizations. Successive solutions are only indirectly
related, via the re-prioritization that results from analyzing the prior
solution. Similarly, successive prioritizations are generated by constructing
and analyzing solutions. This `coupled search' has some interesting properties,
which we discuss. We report encouraging experimental results on two domains,
scheduling problems that arise in fiber-optic cable manufacturing, and graph
coloring problems. The fact that these domains are very different supports our
claim that SWO is a general technique for optimization.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:51:22 GMT"
}
] | 1,306,713,600,000 | [
[
"Clements",
"D. P.",
""
],
[
"Joslin",
"D. E.",
""
]
] |
1105.5455 | D. Barber | D. Barber, P. de van Laar | Variational Cumulant Expansions for Intractable Distributions | null | Journal Of Artificial Intelligence Research, Volume 10, pages
435-455, 1999 | 10.1613/jair.567 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intractable distributions present a common difficulty in inference within the
probabilistic knowledge representation framework and variational methods have
recently been popular in providing an approximate solution. In this article, we
describe a perturbational approach in the form of a cumulant expansion which,
to lowest order, recovers the standard Kullback-Leibler variational bound.
Higher-order terms describe corrections on the variational approach without
incurring much further computational cost. The relationship to other
perturbational approaches such as TAP is also elucidated. We demonstrate the
method on a particular class of undirected graphical models, Boltzmann
machines, for which our simulation results confirm improved accuracy and
enhanced stability during learning.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:51:46 GMT"
}
] | 1,306,713,600,000 | [
[
"Barber",
"D.",
""
],
[
"de van Laar",
"P.",
""
]
] |
1105.5457 | M. Fox | M. Fox, D. Long | Efficient Implementation of the Plan Graph in STAN | null | Journal Of Artificial Intelligence Research, Volume 10, pages
87-115, 1999 | 10.1613/jair.570 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | STAN is a Graphplan-based planner, so-called because it uses a variety of
STate ANalysis techniques to enhance its performance. STAN competed in the
AIPS-98 planning competition where it compared well with the other competitors
in terms of speed, finding solutions fastest to many of the problems posed.
Although the domain analysis techniques STAN exploits are an important factor
in its overall performance, we believe that the speed at which STAN solved the
competition problems is largely due to the implementation of its plan graph.
The implementation is based on two insights: that many of the graph
construction operations can be implemented as bit-level logical operations on
bit vectors, and that the graph should not be explicitly constructed beyond the
fix point. This paper describes the implementation of STAN's plan graph and
provides experimental results which demonstrate the circumstances under which
advantages can be obtained from using this implementation.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:52:09 GMT"
}
] | 1,306,713,600,000 | [
[
"Fox",
"M.",
""
],
[
"Long",
"D.",
""
]
] |
1105.5458 | M. Fuchs | M. Fuchs, D. Fuchs | Cooperation between Top-Down and Bottom-Up Theorem Provers | null | Journal Of Artificial Intelligence Research, Volume 10, pages
169-198, 1999 | 10.1613/jair.573 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Top-down and bottom-up theorem proving approaches each have specific
advantages and disadvantages. Bottom-up provers profit from strong redundancy
control but suffer from the lack of goal-orientation, whereas top-down provers
are goal-oriented but often have weak calculi when their proof lengths are
considered. In order to integrate both approaches, we try to achieve
cooperation between a top-down and a bottom-up prover in two different ways:
The first technique aims at supporting a bottom-up with a top-down prover. A
top-down prover generates subgoal clauses, they are then processed by a
bottom-up prover. The second technique deals with the use of bottom-up
generated lemmas in a top-down prover. We apply our concept to the areas of
model elimination and superposition. We discuss the ability of our techniques
to shorten proofs as well as to reorder the search space in an appropriate
manner. Furthermore, in order to identify subgoal clauses and lemmas which are
actually relevant for the proof task, we develop methods for a relevancy-based
filtering. Experiments with the provers SETHEO and SPASS performed in the
problem library TPTP reveal the high potential of our cooperation approaches.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:52:28 GMT"
}
] | 1,306,713,600,000 | [
[
"Fuchs",
"M.",
""
],
[
"Fuchs",
"D.",
""
]
] |
1105.5459 | T. Hogg | T. Hogg | Solving Highly Constrained Search Problems with Quantum Computers | null | Journal Of Artificial Intelligence Research, Volume 10, pages
39-66, 1999 | 10.1613/jair.574 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A previously developed quantum search algorithm for solving 1-SAT problems in
a single step is generalized to apply to a range of highly constrained k-SAT
problems. We identify a bound on the number of clauses in satisfiability
problems for which the generalized algorithm can find a solution in a constant
number of steps as the number of variables increases. This performance
contrasts with the linear growth in the number of steps required by the best
classical algorithms, and the exponential number required by classical and
quantum methods that ignore the problem structure. In some cases, the algorithm
can also guarantee that insoluble problems in fact have no solutions, unlike
previously proposed quantum search algorithms.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:52:46 GMT"
}
] | 1,306,713,600,000 | [
[
"Hogg",
"T.",
""
]
] |
1105.5460 | C. Boutilier | C. Boutilier, T. Dean, S. Hanks | Decision-Theoretic Planning: Structural Assumptions and Computational
Leverage | null | Journal Of Artificial Intelligence Research, Volume 11, pages
1-94, 1999 | 10.1613/jair.575 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning under uncertainty is a central problem in the study of automated
sequential decision making, and has been addressed by researchers in many
different fields, including AI planning, decision analysis, operations
research, control theory and economics. While the assumptions and perspectives
adopted in these areas often differ in substantial ways, many planning problems
of interest to researchers in these fields can be modeled as Markov decision
processes (MDPs) and analyzed using the techniques of decision theory. This
paper presents an overview and synthesis of MDP-related methods, showing how
they provide a unifying framework for modeling many classes of planning
problems studied in AI. It also describes structural properties of MDPs that,
when exhibited by particular classes of problems, can be exploited in the
construction of optimal or approximately optimal policies or plans. Planning
problems commonly possess structure in the reward and value functions used to
describe performance criteria, in the functions used to describe state
transitions and observations, and in the relationships among features used to
describe states, actions, rewards, and observations. Specialized
representations, and algorithms employing these representations, can achieve
computational leverage by exploiting these various forms of structure. Certain
AI techniques -- in particular those based on the use of structured,
intensional representations -- can be viewed in this way. This paper surveys
several types of representations for both classical and decision-theoretic
planning problems, and planning algorithms that exploit these representations
in a number of different ways to ease the computational burden of constructing
policies or plans. It focuses primarily on abstraction, aggregation and
decomposition techniques based on AI-style representations.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:53:02 GMT"
}
] | 1,306,713,600,000 | [
[
"Boutilier",
"C.",
""
],
[
"Dean",
"T.",
""
],
[
"Hanks",
"S.",
""
]
] |
1105.5461 | T. Lukasiewicz | T. Lukasiewicz | Probabilistic Deduction with Conditional Constraints over Basic Events | null | Journal Of Artificial Intelligence Research, Volume 10, pages
199-241, 1999 | 10.1613/jair.577 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of probabilistic deduction with conditional constraints
over basic events. We show that globally complete probabilistic deduction with
conditional constraints over basic events is NP-hard. We then concentrate on
the special case of probabilistic deduction in conditional constraint trees. We
elaborate very efficient techniques for globally complete probabilistic
deduction. In detail, for conditional constraint trees with point
probabilities, we present a local approach to globally complete probabilistic
deduction, which runs in linear time in the size of the conditional constraint
trees. For conditional constraint trees with interval probabilities, we show
that globally complete probabilistic deduction can be done in a global approach
by solving nonlinear programs. We show how these nonlinear programs can be
transformed into equivalent linear programs, which are solvable in polynomial
time in the size of the conditional constraint trees.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:53:20 GMT"
}
] | 1,306,713,600,000 | [
[
"Lukasiewicz",
"T.",
""
]
] |
1105.5462 | T. S. Jaakkola | T. S. Jaakkola, M. I. Jordan | Variational Probabilistic Inference and the QMR-DT Network | null | Journal Of Artificial Intelligence Research, Volume 10, pages
291-322, 1999 | 10.1613/jair.583 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a variational approximation method for efficient inference in
large-scale probabilistic models. Variational methods are deterministic
procedures that provide approximations to marginal and conditional
probabilities of interest. They provide alternatives to approximate inference
methods based on stochastic sampling or search. We describe a variational
approach to the problem of diagnostic inference in the `Quick Medical
Reference' (QMR) network. The QMR network is a large-scale probabilistic
graphical model built on statistical and expert knowledge. Exact probabilistic
inference is infeasible in this model for all but a small set of cases. We
evaluate our variational inference algorithm on a large set of diagnostic test
cases, comparing the algorithm to a state-of-the-art stochastic sampling
method.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:53:36 GMT"
}
] | 1,306,713,600,000 | [
[
"Jaakkola",
"T. S.",
""
],
[
"Jordan",
"M. I.",
""
]
] |
1105.5463 | A. Borgida | A. Borgida | Extensible Knowledge Representation: the Case of Description Reasoners | null | Journal Of Artificial Intelligence Research, Volume 10, pages
399-434, 1999 | 10.1613/jair.584 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper offers an approach to extensible knowledge representation and
reasoning for a family of formalisms known as Description Logics. The approach
is based on the notion of adding new concept constructors, and includes a
heuristic methodology for specifying the desired extensions, as well as a
modularized software architecture that supports implementing extensions. The
architecture detailed here falls in the normalize-compared paradigm, and
supports both intentional reasoning (subsumption) involving concepts, and
extensional reasoning involving individuals after incremental updates to the
knowledge base. The resulting approach can be used to extend the reasoner with
specialized notions that are motivated by specific problems or application
areas, such as reasoning about dates, plans, etc. In addition, it provides an
opportunity to implement constructors that are not currently yet sufficiently
well understood theoretically, but are needed in practice. Also, for
constructors that are provably hard to reason with (e.g., ones whose presence
would lead to undecidability), it allows the implementation of incomplete
reasoners where the incompleteness is tailored to be acceptable for the
application at hand.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:53:50 GMT"
}
] | 1,306,713,600,000 | [
[
"Borgida",
"A.",
""
]
] |
1105.5465 | J. Rintanen | J. Rintanen | Constructing Conditional Plans by a Theorem-Prover | null | Journal Of Artificial Intelligence Research, Volume 10, pages
323-352, 1999 | 10.1613/jair.591 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The research on conditional planning rejects the assumptions that there is no
uncertainty or incompleteness of knowledge with respect to the state and
changes of the system the plans operate on. Without these assumptions the
sequences of operations that achieve the goals depend on the initial state and
the outcomes of nondeterministic changes in the system. This setting raises the
questions of how to represent the plans and how to perform plan search. The
answers are quite different from those in the simpler classical framework. In
this paper, we approach conditional planning from a new viewpoint that is
motivated by the use of satisfiability algorithms in classical planning.
Translating conditional planning to formulae in the propositional logic is not
feasible because of inherent computational limitations. Instead, we translate
conditional planning to quantified Boolean formulae. We discuss three
formalizations of conditional planning as quantified Boolean formulae, and
present experimental results obtained with a theorem-prover.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:54:30 GMT"
}
] | 1,306,713,600,000 | [
[
"Rintanen",
"J.",
""
]
] |
1105.5466 | K. M. Ting | K. M. Ting, I. H. Witten | Issues in Stacked Generalization | null | Journal Of Artificial Intelligence Research, Volume 10, pages
271-289, 1999 | 10.1613/jair.594 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stacked generalization is a general method of using a high-level model to
combine lower-level models to achieve greater predictive accuracy. In this
paper we address two crucial issues which have been considered to be a `black
art' in classification tasks ever since the introduction of stacked
generalization in 1992 by Wolpert: the type of generalizer that is suitable to
derive the higher-level model, and the kind of attributes that should be used
as its input. We find that best results are obtained when the higher-level
model combines the confidence (and not just the predictions) of the lower-level
ones. We demonstrate the effectiveness of stacked generalization for combining
three different types of learning algorithms for classification tasks. We also
compare the performance of stacked generalization with majority vote and
published results of arcing and bagging.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 01:54:47 GMT"
}
] | 1,306,713,600,000 | [
[
"Ting",
"K. M.",
""
],
[
"Witten",
"I. H.",
""
]
] |
1105.5516 | Fabian Suchanek | Fabian Suchanek (INRIA Saclay - Ile de France), Serge Abiteboul (INRIA
Saclay - Ile de France), Pierre Senellart | Ontology Alignment at the Instance and Schema Level | Technical Report at INRIA RT-0408 | N° RT-0408 (2011) | null | RT-0408 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present PARIS, an approach for the automatic alignment of ontologies.
PARIS aligns not only instances, but also relations and classes. Alignments at
the instance-level cross-fertilize with alignments at the schema-level.
Thereby, our system provides a truly holistic solution to the problem of
ontology alignment. The heart of the approach is probabilistic. This allows
PARIS to run without any parameter tuning. We demonstrate the efficiency of the
algorithm and its precision through extensive experiments. In particular, we
obtain a precision of around 90% in experiments with two of the world's largest
ontologies.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 10:18:08 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2011 12:38:05 GMT"
},
{
"version": "v3",
"created": "Thu, 18 Aug 2011 13:00:27 GMT"
}
] | 1,313,712,000,000 | [
[
"Suchanek",
"Fabian",
"",
"INRIA Saclay - Ile de France"
],
[
"Abiteboul",
"Serge",
"",
"INRIA\n Saclay - Ile de France"
],
[
"Senellart",
"Pierre",
""
]
] |
1105.5667 | Nina Narodytska | Jessica Davies, George Katsirelos, Nina Narodytska, Toby Walsh | Complexity of and Algorithms for Borda Manipulation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We prove that it is NP-hard for a coalition of two manipulators to compute
how to manipulate the Borda voting rule. This resolves one of the last open
problems in the computational complexity of manipulating common voting rules.
Because of this NP-hardness, we treat computing a manipulation as an
approximation problem where we try to minimize the number of manipulators.
Based on ideas from bin packing and multiprocessor scheduling, we propose two
new approximation methods to compute manipulations of the Borda rule.
Experiments show that these methods significantly outperform the previous best
known %existing approximation method. We are able to find optimal manipulations
in almost all the randomly generated elections tested. Our results suggest
that, whilst computing a manipulation of the Borda rule by a coalition is
NP-hard, computational complexity may provide only a weak barrier against
manipulation in practice.
| [
{
"version": "v1",
"created": "Fri, 27 May 2011 23:11:40 GMT"
}
] | 1,306,800,000,000 | [
[
"Davies",
"Jessica",
""
],
[
"Katsirelos",
"George",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Walsh",
"Toby",
""
]
] |
1105.6124 | F. Barber | F. Barber | Reasoning on Interval and Point-based Disjunctive Metric Constraints in
Temporal Contexts | null | Journal Of Artificial Intelligence Research, Volume 12, pages
35-86, 2000 | 10.1613/jair.693 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a temporal model for reasoning on disjunctive metric constraints
on intervals and time points in temporal contexts. This temporal model is
composed of a labeled temporal algebra and its reasoning algorithms. The
labeled temporal algebra defines labeled disjunctive metric point-based
constraints, where each disjunct in each input disjunctive constraint is
univocally associated to a label. Reasoning algorithms manage labeled
constraints, associated label lists, and sets of mutually inconsistent
disjuncts. These algorithms guarantee consistency and obtain a minimal network.
Additionally, constraints can be organized in a hierarchy of alternative
temporal contexts. Therefore, we can reason on context-dependent disjunctive
metric constraints on intervals and points. Moreover, the model is able to
represent non-binary constraints, such that logical dependencies on disjuncts
in constraints can be handled. The computational cost of reasoning algorithms
is exponential in accordance with the underlying problem complexity, although
some improvements are proposed.
| [
{
"version": "v1",
"created": "Mon, 30 May 2011 22:09:11 GMT"
}
] | 1,306,886,400,000 | [
[
"Barber",
"F.",
""
]
] |
1105.6148 | Mohammed El-Dosuky | M. A. El-Dosuky, T. T. Hamza, M. Z. Rashad and A. H. Naguib | Overcoming Misleads In Logic Programs by Redefining Negation | 8 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Negation as failure and incomplete information in logic programs have been
studied by many researchers In order to explains HOW a negated conclusion was
reached, we introduce and proof a different way for negating facts to
overcoming misleads in logic programs. Negating facts can be achieved by asking
the user for constants that do not appear elsewhere in the knowledge base.
| [
{
"version": "v1",
"created": "Tue, 31 May 2011 02:19:21 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Mar 2013 23:40:11 GMT"
}
] | 1,362,528,000,000 | [
[
"El-Dosuky",
"M. A.",
""
],
[
"Hamza",
"T. T.",
""
],
[
"Rashad",
"M. Z.",
""
],
[
"Naguib",
"A. H.",
""
]
] |
1106.0171 | Gilberto de Paiva | Gilberto de Paiva | Proposal of Pattern Recognition as a necessary and sufficient Principle
to Cognitive Science | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the prevalence of the Computational Theory of Mind and the
Connectionist Model, the establishing of the key principles of the Cognitive
Science are still controversy and inconclusive. This paper proposes the concept
of Pattern Recognition as Necessary and Sufficient Principle for a general
cognitive science modeling, in a very ambitious scientific proposal. A formal
physical definition of the pattern recognition concept is also proposed to
solve many key conceptual gaps on the field.
| [
{
"version": "v1",
"created": "Tue, 31 May 2011 06:40:52 GMT"
}
] | 1,306,972,800,000 | [
[
"de Paiva",
"Gilberto",
""
]
] |
1106.0218 | E. Birnbaum | E. Birnbaum, E. L. Lozinskii | The Good Old Davis-Putnam Procedure Helps Counting Models | null | Journal Of Artificial Intelligence Research, Volume 10, pages
457-477, 1999 | 10.1613/jair.601 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As was shown recently, many important AI problems require counting the number
of models of propositional formulas. The problem of counting models of such
formulas is, according to present knowledge, computationally intractable in a
worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP,
that computes the exact number of models of a propositional CNF or DNF formula
F. Let m and n be the number of clauses and variables of F, respectively, and
let p denote the probability that a literal l of F occurs in a clause C of F,
then the average running time of CDP is shown to be O(nm^d), where
d=-1/log(1-p). The practical performance of CDP has been estimated in a series
of experiments on a wide variety of CNF formulas.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:14:46 GMT"
}
] | 1,306,972,800,000 | [
[
"Birnbaum",
"E.",
""
],
[
"Lozinskii",
"E. L.",
""
]
] |
1106.0219 | C. E. Brodley | C. E. Brodley, M. A. Friedl | Identifying Mislabeled Training Data | null | Journal Of Artificial Intelligence Research, Volume 11, pages
131-167, 1999 | 10.1613/jair.606 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new approach to identifying and eliminating mislabeled
training instances for supervised learning. The goal of this approach is to
improve classification accuracies produced by learning algorithms by improving
the quality of the training data. Our approach uses a set of learning
algorithms to create classifiers that serve as noise filters for the training
data. We evaluate single algorithm, majority vote and consensus filters on five
datasets that are prone to labeling errors. Our experiments illustrate that
filtering significantly improves classification accuracy for noise levels up to
30 percent. An analytical and empirical evaluation of the precision of our
approach shows that consensus filters are conservative at throwing away good
data at the expense of retaining bad data and that majority filters are better
at detecting bad data at the expense of throwing away good data. This suggests
that for situations in which there is a paucity of data, consensus filters are
preferable, whereas majority vote filters are preferable for situations with an
abundance of data.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:15:28 GMT"
}
] | 1,306,972,800,000 | [
[
"Brodley",
"C. E.",
""
],
[
"Friedl",
"M. A.",
""
]
] |
1106.0220 | S. Argamon-Engelson | S. Argamon-Engelson, I. Dagan | Committee-Based Sample Selection for Probabilistic Classifiers | null | Journal Of Artificial Intelligence Research, Volume 11, pages
335-360, 1999 | 10.1613/jair.612 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:15:56 GMT"
}
] | 1,306,972,800,000 | [
[
"Argamon-Engelson",
"S.",
""
],
[
"Dagan",
"I.",
""
]
] |
1106.0224 | R. Rosati | R. Rosati | Reasoning about Minimal Belief and Negation as Failure | null | Journal Of Artificial Intelligence Research, Volume 11, pages
277-300, 1999 | 10.1613/jair.637 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the problem of reasoning in the propositional fragment of
MBNF, the logic of minimal belief and negation as failure introduced by
Lifschitz, which can be considered as a unifying framework for several
nonmonotonic formalisms, including default logic, autoepistemic logic,
circumscription, epistemic queries, and logic programming. We characterize the
complexity and provide algorithms for reasoning in propositional MBNF. In
particular, we show that entailment in propositional MBNF lies at the third
level of the polynomial hierarchy, hence it is harder than reasoning in all the
above mentioned propositional formalisms for nonmonotonic reasoning. We also
prove the exact correspondence between negation as failure in MBNF and negative
introspection in Moore's autoepistemic logic.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:17:18 GMT"
}
] | 1,306,972,800,000 | [
[
"Rosati",
"R.",
""
]
] |
1106.0225 | R. Bar-Yehuda | R. Bar-Yehuda, A. Becker, D. Geiger | Randomized Algorithms for the Loop Cutset Problem | null | Journal Of Artificial Intelligence Research, Volume 12, pages
219-234, 2000 | 10.1613/jair.638 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show how to find a minimum weight loop cutset in a Bayesian network with
high probability. Finding such a loop cutset is the first step in the method of
conditioning for inference. Our randomized algorithm for finding a loop cutset
outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least
1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is
the minimal size of a minimum weight loop cutset, and n is the number of
vertices. We also show empirically that a variant of this algorithm often finds
a loop cutset that is closer to the minimum weight loop cutset than the ones
found by the best deterministic algorithms known.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:17:38 GMT"
}
] | 1,306,972,800,000 | [
[
"Bar-Yehuda",
"R.",
""
],
[
"Becker",
"A.",
""
],
[
"Geiger",
"D.",
""
]
] |
1106.0229 | R. M. Jensen | R. M. Jensen, M. M. Veloso | OBDD-based Universal Planning for Synchronized Agents in
Non-Deterministic Domains | null | Journal Of Artificial Intelligence Research, Volume 13, pages
189-226, 2000 | 10.1613/jair.649 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently model checking representation and search techniques were shown to be
efficiently applicable to planning, in particular to non-deterministic
planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs)
to encode a planning domain as a non-deterministic finite automaton and then
apply fast algorithms from model checking to search for a solution. OBDDs can
effectively scale and can provide universal plans for complex planning domains.
We are particularly interested in addressing the complexities arising in
non-deterministic, multi-agent domains. In this article, we present UMOP, a new
universal OBDD-based planning framework for non-deterministic, multi-agent
domains. We introduce a new planning domain description language, NADL, to
specify non-deterministic, multi-agent domains. The language contributes the
explicit definition of controllable agents and uncontrollable environment
agents. We describe the syntax and semantics of NADL and show how to build an
efficient OBDD-based representation of an NADL description. The UMOP planning
system uses NADL and different OBDD-based universal planning algorithms. It
includes the previously developed strong and strong cyclic planning algorithms.
In addition, we introduce our new optimistic planning algorithm that relaxes
optimality guarantees and generates plausible universal plans in some domains
where no strong nor strong cyclic solution exists. We present empirical results
applying UMOP to domains ranging from deterministic and single-agent with no
environment actions to non-deterministic and multi-agent with complex
environment actions. UMOP is shown to be a rich and efficient planning system.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:22:36 GMT"
}
] | 1,306,972,800,000 | [
[
"Jensen",
"R. M.",
""
],
[
"Veloso",
"M. M.",
""
]
] |
1106.0230 | S. Kambhampati | S. Kambhampati | Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP
Search Techniques in Graphplan | null | Journal Of Artificial Intelligence Research, Volume 12, pages
1-34, 2000 | 10.1613/jair.655 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper reviews the connections between Graphplan's planning-graph and the
dynamic constraint satisfaction problem and motivates the need for adapting CSP
search techniques to the Graphplan algorithm. It then describes how explanation
based learning, dependency directed backtracking, dynamic variable ordering,
forward checking, sticky values and random-restart search strategies can be
adapted to Graphplan. Empirical results are provided to demonstrate that these
augmentations improve Graphplan's performance significantly (up to 1000x
speedups) on several benchmark problems. Special attention is paid to the
explanation-based learning and dependency directed backtracking techniques as
they are empirically found to be most useful in improving the performance of
Graphplan.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:22:50 GMT"
}
] | 1,306,972,800,000 | [
[
"Kambhampati",
"S.",
""
]
] |
1106.0233 | M. Cadoli | M. Cadoli, F. M. Donini, P. Liberatore, M. Schaerf | Space Efficiency of Propositional Knowledge Representation Formalisms | null | Journal Of Artificial Intelligence Research, Volume 13, pages
1-31, 2000 | 10.1613/jair.664 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the space efficiency of a Propositional Knowledge
Representation (PKR) formalism. Intuitively, the space efficiency of a
formalism F in representing a certain piece of knowledge A, is the size of the
shortest formula of F that represents A. In this paper we assume that knowledge
is either a set of propositional interpretations (models) or a set of
propositional formulae (theorems). We provide a formal way of talking about the
relative ability of PKR formalisms to compactly represent a set of models or a
set of theorems. We introduce two new compactness measures, the corresponding
classes, and show that the relative space efficiency of a PKR formalism in
representing models/theorems is directly related to such classes. In
particular, we consider formalisms for nonmonotonic reasoning, such as
circumscription and default logic, as well as belief revision operators and the
stable model semantics for logic programs with negation. One interesting result
is that formalisms with the same time complexity do not necessarily belong to
the same space efficiency class.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:24:29 GMT"
}
] | 1,306,972,800,000 | [
[
"Cadoli",
"M.",
""
],
[
"Donini",
"F. M.",
""
],
[
"Liberatore",
"P.",
""
],
[
"Schaerf",
"M.",
""
]
] |
1106.0234 | M. Hauskrecht | M. Hauskrecht | Value-Function Approximations for Partially Observable Markov Decision
Processes | null | Journal Of Artificial Intelligence Research, Volume 13, pages
33-94, 2000 | 10.1613/jair.678 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partially observable Markov decision processes (POMDPs) provide an elegant
mathematical framework for modeling complex decision and planning problems in
stochastic domains in which states of the system are observable only
indirectly, via a set of imperfect or noisy observations. The modeling
advantage of POMDPs, however, comes at a price -- exact methods for solving
them are computationally very expensive and thus applicable in practice only to
very simple problems. We focus on efficient approximation (heuristic) methods
that attempt to alleviate the computational problem and trade off accuracy for
speed. We have two objectives here. First, we survey various approximation
methods, analyze their properties and relations and provide some new insights
into their differences. Second, we present a number of new approximation
methods and novel refinements of existing techniques. The theoretical results
are supported by experiments on a problem from the agent navigation domain.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:24:43 GMT"
}
] | 1,306,972,800,000 | [
[
"Hauskrecht",
"M.",
""
]
] |
1106.0237 | R. M. Neal | R. M. Neal | On Deducing Conditional Independence from d-Separation in Causal Graphs
with Feedback (Research Note) | null | Journal Of Artificial Intelligence Research, Volume 12, pages
87-91, 2000 | 10.1613/jair.689 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pearl and Dechter (1996) claimed that the d-separation criterion for
conditional independence in acyclic causal networks also applies to networks of
discrete variables that have feedback cycles, provided that the variables of
the system are uniquely determined by the random disturbances. I show by
example that this is not true in general. Some condition stronger than
uniqueness is needed, such as the existence of a causal dynamics guaranteed to
lead to the unique solution.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:36:47 GMT"
}
] | 1,306,972,800,000 | [
[
"Neal",
"R. M.",
""
]
] |
1106.0238 | A. Borgida | A. Borgida, R. Kusters | What's in an Attribute? Consequences for the Least Common Subsumer | null | Journal Of Artificial Intelligence Research, Volume 14, pages
167-203, 2001 | 10.1613/jair.702 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Functional relationships between objects, called `attributes', are of
considerable importance in knowledge representation languages, including
Description Logics (DLs). A study of the literature indicates that papers have
made, often implicitly, different assumptions about the nature of attributes:
whether they are always required to have a value, or whether they can be
partial functions. The work presented here is the first explicit study of this
difference for subclasses of the CLASSIC DL, involving the same-as concept
constructor. It is shown that although determining subsumption between concept
descriptions has the same complexity (though requiring different algorithms),
the story is different in the case of determining the least common subsumer
(lcs). For attributes interpreted as partial functions, the lcs exists and can
be computed relatively easily; even in this case our results correct and extend
three previous papers about the lcs of DLs. In the case where attributes must
have a value, the lcs may not exist, and even if it exists it may be of
exponential size. Interestingly, it is possible to decide in polynomial time if
the lcs exists.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:36:59 GMT"
}
] | 1,306,972,800,000 | [
[
"Borgida",
"A.",
""
],
[
"Kusters",
"R.",
""
]
] |
1106.0239 | S. Tobies | S. Tobies | The Complexity of Reasoning with Cardinality Restrictions and Nominals
in Expressive Description Logics | null | Journal Of Artificial Intelligence Research, Volume 12, pages
199-217, 2000 | 10.1613/jair.705 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the complexity of the combination of the Description Logics ALCQ and
ALCQI with a terminological formalism based on cardinality restrictions on
concepts. These combinations can naturally be embedded into C^2, the two
variable fragment of predicate logic with counting quantifiers, which yields
decidability in NExpTime. We show that this approach leads to an optimal
solution for ALCQI, as ALCQI with cardinality restrictions has the same
complexity as C^2 (NExpTime-complete). In contrast, we show that for ALCQ, the
problem can be solved in ExpTime. This result is obtained by a reduction of
reasoning with cardinality restrictions to reasoning with the (in general
weaker) terminological formalism of general axioms for ALCQ extended with
nominals. Using the same reduction, we show that, for the extension of ALCQI
with nominals, reasoning with general axioms is a NExpTime-complete problem.
Finally, we sharpen this result and show that pure concept satisfiability for
ALCQI with nominals is NExpTime-complete. Without nominals, this problem is
known to be PSpace-complete.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:37:11 GMT"
}
] | 1,306,972,800,000 | [
[
"Tobies",
"S.",
""
]
] |
1106.0240 | I. P. Gent | I. P. Gent, J. Singer, A. Smaill | Backbone Fragility and the Local Search Cost Peak | null | Journal Of Artificial Intelligence Research, Volume 12, pages
235-270, 2000 | 10.1613/jair.711 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The local search algorithm WSat is one of the most successful algorithms for
solving the satisfiability (SAT) problem. It is notably effective at solving
hard Random 3-SAT instances near the so-called `satisfiability threshold', but
still shows a peak in search cost near the threshold and large variations in
cost over different instances. We make a number of significant contributions to
the analysis of WSat on high-cost random instances, using the
recently-introduced concept of the backbone of a SAT instance. The backbone is
the set of literals which are entailed by an instance. We find that the number
of solutions predicts the cost well for small-backbone instances but is much
less relevant for the large-backbone instances which appear near the threshold
and dominate in the overconstrained region. We show a very strong correlation
between search cost and the Hamming distance to the nearest solution early in
WSat's search. This pattern leads us to introduce a measure of the backbone
fragility of an instance, which indicates how persistent the backbone is as
clauses are removed. We propose that high-cost random instances for local
search are those with very large backbones which are also backbone-fragile. We
suggest that the decay in cost beyond the satisfiability threshold is due to
increasing backbone robustness (the opposite of backbone fragility). Our
hypothesis makes three correct predictions. First, that the backbone robustness
of an instance is negatively correlated with the local search cost when other
factors are controlled for. Second, that backbone-minimal instances (which are
3-SAT instances altered so as to be more backbone-fragile) are unusually hard
for WSat. Third, that the clauses most often unsatisfied during search are
those whose deletion has the most effect on the backbone. In understanding the
pathologies of local search methods, we hope to contribute to the development
of new and better techniques.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:37:25 GMT"
}
] | 1,306,972,800,000 | [
[
"Gent",
"I. P.",
""
],
[
"Singer",
"J.",
""
],
[
"Smaill",
"A.",
""
]
] |
1106.0241 | M. A. Walker | M. A. Walker | An Application of Reinforcement Learning to Dialogue Strategy Selection
in a Spoken Dialogue System for Email | null | Journal Of Artificial Intelligence Research, Volume 12, pages
387-416, 2000 | 10.1613/jair.713 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a novel method by which a spoken dialogue system can
learn to choose an optimal dialogue strategy from its experience interacting
with human users. The method is based on a combination of reinforcement
learning and performance modeling of spoken dialogue systems. The reinforcement
learning component applies Q-learning (Watkins, 1989), while the performance
modeling component applies the PARADISE evaluation framework (Walker et al.,
1997) to learn the performance function (reward) used in reinforcement
learning. We illustrate the method with a spoken dialogue system named ELVIS
(EmaiL Voice Interactive System), that supports access to email over the phone.
We conduct a set of experiments for training an optimal dialogue strategy on a
corpus of 219 dialogues in which human users interact with ELVIS over the
phone. We then test that strategy on a corpus of 18 dialogues. We show that
ELVIS can learn to optimize its strategy selection for agent initiative, for
reading messages, and for summarizing email folders.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:37:37 GMT"
}
] | 1,306,972,800,000 | [
[
"Walker",
"M. A.",
""
]
] |
1106.0242 | J. Goldsmith | J. Goldsmith, C. Lusena, M. Mundhenk | Nonapproximability Results for Partially Observable Markov Decision
Processes | null | Journal Of Artificial Intelligence Research, Volume 14, pages
83-103, 2001 | 10.1613/jair.714 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that for several variations of partially observable Markov decision
processes, polynomial-time algorithms for finding control policies are unlikely
to or simply don't have guarantees of finding policies within a constant factor
or a constant summand of optimal. Here "unlikely" means "unless some complexity
classes collapse," where the collapses considered are P=NP, P=PSPACE, or P=EXP.
Until or unless these collapses are shown to hold, any control-policy designer
must choose between such performance guarantees and efficient computation.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:37:53 GMT"
}
] | 1,306,972,800,000 | [
[
"Goldsmith",
"J.",
""
],
[
"Lusena",
"C.",
""
],
[
"Mundhenk",
"M.",
""
]
] |
1106.0243 | J. Hoffmann | J. Hoffmann, J. Koehler | On Reasonable and Forced Goal Orderings and their Use in an
Agenda-Driven Planning Algorithm | null | Journal Of Artificial Intelligence Research, Volume 12, pages
338-386, 2000 | 10.1613/jair.715 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper addresses the problem of computing goal orderings, which is one of
the longstanding issues in AI planning. It makes two new contributions. First,
it formally defines and discusses two different goal orderings, which are
called the reasonable and the forced ordering. Both orderings are defined for
simple STRIPS operators as well as for more complex ADL operators supporting
negation and conditional effects. The complexity of these orderings is
investigated and their practical relevance is discussed. Secondly, two
different methods to compute reasonable goal orderings are developed. One of
them is based on planning graphs, while the other investigates the set of
actions directly. Finally, it is shown how the ordering relations, which have
been derived for a given set of goals G, can be used to compute a so-called
goal agenda that divides G into an ordered set of subgoals. Any planner can
then, in principle, use the goal agenda to plan for increasing sets of
subgoals. This can lead to an exponential complexity reduction, as the solution
to a complex planning problem is found by solving easier subproblems. Since
only a polynomial overhead is caused by the goal agenda computation, a
potential exists to dramatically speed up planning algorithms as we demonstrate
in the empirical evaluation, where we use this method in the IPP planner.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:38:06 GMT"
}
] | 1,306,972,800,000 | [
[
"Hoffmann",
"J.",
""
],
[
"Koehler",
"J.",
""
]
] |
1106.0244 | D. F. Gordon | D. F. Gordon | Asimovian Adaptive Agents | null | Journal Of Artificial Intelligence Research, Volume 13, pages
95-153, 2000 | 10.1613/jair.720 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this research is to develop agents that are adaptive and
predictable and timely. At first blush, these three requirements seem
contradictory. For example, adaptation risks introducing undesirable side
effects, thereby making agents' behavior less predictable. Furthermore,
although formal verification can assist in ensuring behavioral predictability,
it is known to be time-consuming. Our solution to the challenge of satisfying
all three requirements is the following. Agents have finite-state automaton
plans, which are adapted online via evolutionary learning (perturbation)
operators. To ensure that critical behavioral constraints are always satisfied,
agents' plans are first formally verified. They are then reverified after every
adaptation. If reverification concludes that constraints are violated, the
plans are repaired. The main objective of this paper is to improve the
efficiency of reverification after learning, so that agents have a sufficiently
rapid response time. We present two solutions: positive results that certain
learning operators are a priori guaranteed to preserve useful classes of
behavioral assurance constraints (which implies that no reverification is
needed for these operators), and efficient incremental reverification
algorithms for those learning operators that have negative a priori results.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:38:25 GMT"
}
] | 1,306,972,800,000 | [
[
"Gordon",
"D. F.",
""
]
] |
1106.0245 | J. Baxter | J. Baxter | A Model of Inductive Bias Learning | null | Journal Of Artificial Intelligence Research, Volume 12, pages
149-198, 2000 | 10.1613/jair.731 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major problem in machine learning is that of inductive bias: how to choose
a learner's hypothesis space so that it is large enough to contain a solution
to the problem being learnt, yet small enough to ensure reliable generalization
from reasonably-sized training sets. Typically such bias is supplied by hand
through the skill and insights of experts. In this paper a model for
automatically learning bias is investigated. The central assumption of the
model is that the learner is embedded within an environment of related learning
tasks. Within such an environment the learner can sample from multiple tasks,
and hence it can search for a hypothesis space that contains good solutions to
many of the problems in the environment. Under certain restrictions on the set
of all hypothesis spaces available to the learner, we show that a hypothesis
space that performs well on a sufficiently large number of training tasks will
also perform well when learning novel tasks in the same environment. Explicit
bounds are also derived demonstrating that learning multiple tasks within an
environment of related tasks can potentially give much better generalization
than learning a single task.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:38:38 GMT"
}
] | 1,306,972,800,000 | [
[
"Baxter",
"J.",
""
]
] |
1106.0246 | C. Bhattacharyya | C. Bhattacharyya, S. S. Keerthi | Mean Field Methods for a Special Class of Belief Networks | null | Journal Of Artificial Intelligence Research, Volume 15, pages
91-114, 2001 | 10.1613/jair.734 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The chief aim of this paper is to propose mean-field approximations for a
broad class of Belief networks, of which sigmoid and noisy-or networks can be
seen as special cases. The approximations are based on a powerful mean-field
theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach
is the first order approximation in Plefka's approach, via a variational
derivation. The application of Plefka's theory to belief networks is not
computationally tractable. To tackle this problem we propose new approximations
based on Taylor series. Small scale experiments show that the proposed schemes
are attractive.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:38:54 GMT"
}
] | 1,306,972,800,000 | [
[
"Bhattacharyya",
"C.",
""
],
[
"Keerthi",
"S. S.",
""
]
] |
1106.0247 | B. Nebel | B. Nebel | On the Compilability and Expressive Power of Propositional Planning
Formalisms | null | Journal Of Artificial Intelligence Research, Volume 12, pages
271-315, 2000 | 10.1613/jair.735 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent approaches of extending the GRAPHPLAN algorithm to handle more
expressive planning formalisms raise the question of what the formal meaning of
"expressive power" is. We formalize the intuition that expressive power is a
measure of how concisely planning domains and plans can be expressed in a
particular formalism by introducing the notion of "compilation schemes" between
planning formalisms. Using this notion, we analyze the expressiveness of a
large family of propositional planning formalisms, ranging from basic STRIPS to
a formalism with conditional effects, partial state specifications, and
propositional formulae in the preconditions. One of the results is that
conditional effects cannot be compiled away if plan size should grow only
linearly but can be compiled away if we allow for polynomial growth of the
resulting plans. This result confirms that the recently proposed extensions to
the GRAPHPLAN algorithm concerning conditional effects are optimal with respect
to the "compilability" framework. Another result is that general propositional
formulae cannot be compiled into conditional effects if the plan size should be
preserved linearly. This implies that allowing general propositional formulae
in preconditions and effect conditions adds another level of difficulty in
generating a plan.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:39:07 GMT"
}
] | 1,306,972,800,000 | [
[
"Nebel",
"B.",
""
]
] |
1106.0249 | C. Boutilier | C. Boutilier, R. I. Brafman | Partial-Order Planning with Concurrent Interacting Actions | null | Journal Of Artificial Intelligence Research, Volume 14, pages
105-136, 2001 | 10.1613/jair.740 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to generate plans for agents with multiple actuators, agent teams,
or distributed controllers, we must be able to represent and plan using
concurrent actions with interacting effects. This has historically been
considered a challenging task requiring a temporal planner with the ability to
reason explicitly about time. We show that with simple modifications, the
STRIPS action representation language can be used to represent interacting
actions. Moreover, algorithms for partial-order planning require only small
modifications in order to be applied in such multiagent domains. We demonstrate
this fact by developing a sound and complete partial-order planner for planning
with concurrent interacting actions, POMP, that extends existing partial-order
planners in a straightforward way. These results open the way to the use of
partial-order planners for the centralized control of cooperative multiagent
systems.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:39:53 GMT"
}
] | 1,306,972,800,000 | [
[
"Boutilier",
"C.",
""
],
[
"Brafman",
"R. I.",
""
]
] |
1106.0250 | J. L. Ambite | J. L. Ambite, C. A. Knoblock | Planning by Rewriting | null | Journal Of Artificial Intelligence Research, Volume 15, pages
207-261, 2001 | 10.1613/jair.754 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain-independent planning is a hard combinatorial problem. Taking into
account plan quality makes the task even more difficult. This article
introduces Planning by Rewriting (PbR), a new paradigm for efficient
high-quality domain-independent planning. PbR exploits declarative
plan-rewriting rules and efficient local search techniques to transform an
easy-to-generate, but possibly suboptimal, initial plan into a high-quality
plan. In addition to addressing the issues of planning efficiency and plan
quality, this framework offers a new anytime planning algorithm. We have
implemented this planner and applied it to several existing domains. The
experimental results show that the PbR approach provides significant savings in
planning effort while generating high-quality plans.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2011 16:40:10 GMT"
}
] | 1,306,972,800,000 | [
[
"Ambite",
"J. L.",
""
],
[
"Knoblock",
"C. A.",
""
]
] |
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