Modeling Adaptive Autonomous Agents

1993 ◽  
Vol 1 (1_2) ◽  
pp. 135-162 ◽  
Author(s):  
Pattie Maes
Keyword(s):  
Animal Behavior ◽  
Artificial Life ◽  
Autonomous Agents ◽  
State Of The Art ◽  
Time Dependent ◽  
New Wave ◽  
Open Problems ◽  
New Approach ◽  
Unpredictable Environment ◽  
Main Ideas

One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems.

ALIFE: A MULTIAGENT COMPUTING PARADIGM FOR CONSTRAINT SATISFACTION PROBLEMS

2001 ◽  
Vol 15 (03) ◽  
pp. 475-491 ◽  
Author(s):  
JIMING LIU ◽  
HAN JING
Keyword(s):  
Artificial Life ◽  
Large Scale ◽  
Autonomous Agents ◽  
Constraint Satisfaction Problems ◽  
Living Environment ◽  
Distributed Agents ◽  
New Approach ◽  
Agent Based ◽  
Computing Paradigm ◽  
Behavioral Rules

This paper presents a new approach to solving N-queen problems, which involves a model of distributed autonomous agents with artificial life (ALIFE) and a method of representing N-queen constraints in an agent environment. The distributed agents locally interact with their living environment, i.e. a chessboard, and execute their reactive behaviors by applying their behavioral rules for randomized motion, least-conflict position searching, and cooperating with other agents, etc. The agent-based N-queen problem solving system evolves through selection and contest, in which some agents will die or be eaten if their moving strategies are less effective than others. The experimental results have shown that this system is capable of solving large-scale N-queen problems. This paper also provides a model of ALIFE agents for solving general CSPs.

Toward Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life

1993 ◽  
Vol 1 (1_2) ◽  
pp. 111-134 ◽  
Author(s):  
Michael G. Dyer
Keyword(s):  
Animal Behavior ◽  
Artificial Life ◽  
Cooperative Behavior ◽  
Learning Processes ◽  
Open Problems ◽  
Developmental Learning ◽  
Biological Perspective ◽  
Intelligent Behavior ◽  
High Degree ◽  
Open Issues

The tasks that animals perform require a high degree of intelligence. Animals forage for food, migrate, navigate, court mates, rear offspring, defend against predators, construct nests, and so on. These tasks commonly require social interaction/cooperation and are accomplished by animal nervous systems, which are the result of billions of years of evolution and complex developmental/learning processes. The Artificial Life (AL) approach to synthesizing intelligent behavior is guided by this biological perspective. In this article we examine some of the numerous open problems in synthesizing intelligent animal behavior (especially cooperative behavior involving communication) that face the field of AL, a discipline still in its infancy.

A new approach based on graph matching and evolutionary approach for sport scheduling problem

2020 ◽  
pp. 1-16
Author(s):  
Meriem Khelifa ◽  
Dalila Boughaci ◽  
Esma Aïmeur
Keyword(s):  
Graph Matching ◽  
State Of The Art ◽  
Travel Cost ◽  
Round Robin ◽  
New Approach ◽  
Traveling Tournament Problem ◽  
Significant Interest ◽  
National League ◽  
Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

scholarly journals Bin2vec: learning representations of binary executable programs for security tasks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shushan Arakelyan ◽  
Sima Arasteh ◽  
Christophe Hauser ◽  
Erik Kline ◽  
Aram Galstyan
Keyword(s):  
Program Analysis ◽  
State Of The Art ◽  
Classification Error ◽  
New Approach ◽  
Convolutional Networks ◽  
Computational Program ◽  
Functional Algorithm ◽  
Binary Program ◽  
Vulnerability Discovery ◽  
Executable Programs

AbstractTackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

A Single Technically Consistent Design Formula for the Thickness of Cylindrical Sections Under Internal Pressure

2006 ◽  
Vol 129 (1) ◽  
pp. 211-215 ◽  
Author(s):  
John D. Fishburn
Keyword(s):  
Experimental Data ◽  
Internal Pressure ◽  
State Of The Art ◽  
Original Data ◽  
Pressure Vessels ◽  
Time Dependent ◽  
Design Codes ◽  
Recent Developments ◽  
Current Design ◽  
Single Formula

Within the current design codes for boilers, piping, and pressure vessels, there are many different equations for the thickness of a cylindrical section under internal pressure. A reassessment of these various formulations, using the original data, is described together with more recent developments in the state of the art. A single formula, which can be demonstrated to retain the same design margin in both the time-dependent and time-independent regimes, is shown to give the best correlation with the experimental data and is proposed for consideration for inclusion in the design codes.

Extended Molecular Evolutionary Biology: Artificial Life Bridging the Gap Between Chemistry and Biology

1993 ◽  
Vol 1 (1_2) ◽  
pp. 39-60 ◽  
Author(s):  
P. Schuster
Keyword(s):  
Molecular Evolution ◽  
Artificial Life ◽  
Evolutionary Biology ◽  
State Of The Art ◽  
Test Tube ◽  
Base Pairs ◽  
Current State ◽  
Stability Of Structures ◽  
Novel Applications ◽  
Variation And Selection

Molecular evolution provides an ample field for the extension of Nature's principles towards novel applications. Several examples are discussed here, among them are evolution in the test tube, nucleotide chemistry with new base pairs and new backbones, enzyme-free replication of polynucleotides and template chemistry aiming at replicating structures that have nothing in common with the molecules from nature. Molecular evolution in the test tube provides a uniquely simple system for the study of evolutionary phenomena: genotype and phenotype are two features of one and the same RNA molecule. Then fitness landscapes are nothing more than combined mappings from sequences to structures and from structures to functions, the latter being expressed in terms of rate constants. RNA landscapes are presented as examples for which an access to phenomena in reality by mathematical analysis and computer simulations is feasible. New questions concerning stability of structures in evolution can be raised and quantitative answers are given. Evolutionary biotechnology is a spin-off from molecular evolution. Darwin's principle of variation and selection is applied to design novel biopolymers with predetermined functions. Different approaches to achieve this goal are discussed and a survey of the current state of the art is given.

scholarly journals TripletProt: Deep Representation Learning of Proteins based on Siamese Networks

2020 ◽  
Author(s):  
Esmaeil Nourani ◽  
Ehsaneddin Asgari ◽  
Alice C. McHardy ◽  
Mohammad R.K. Mofrad
Keyword(s):  
Functional Annotation ◽  
Cellular Localization ◽  
State Of The Art ◽  
Language Model ◽  
Representation Learning ◽  
Learning Problems ◽  
Ppi Network ◽  
New Approach ◽  
Protein Protein Interaction ◽  
Siamese Networks

AbstractWe introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. We evaluate TripletProt comprehensively in protein functional annotation tasks including sub-cellular localization (14 categories) and gene ontology prediction (more than 2000 classes), which are both challenging multi-class multi-label classification machine learning problems. We compare the performance of TripletProt with the state-of-the-art approaches including recurrent language model-based approach (i.e., UniRep), as well as protein-protein interaction (PPI) network and sequence-based method (i.e., DeepGO). Our TripletProt showed an overall improvement of F1 score in the above mentioned comprehensive functional annotation tasks, solely relying on the PPI network. TripletProt and in general Siamese Network offer great potentials for the protein informatics tasks and can be widely applied to similar tasks.

scholarly journals Graph Algorithms for Word Sense Disambiguation in Biomedicine

2015 ◽  
Author(s):  
Rodrigo Goulart ◽  
Juliano De Carvalho ◽  
Vera De Lima
Keyword(s):  
Text Mining ◽  
Graph Algorithms ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
The State ◽  
Word Sense ◽  
New Approach ◽  
Similar Performance ◽  
Sense Disambiguation ◽  
Different Levels

Word Sense Disambiguation (WSD) is an important task for Biomedicine text-mining. Supervised WSD methods have the best results but they are complex and their cost for testing is too high. This work presents an experiment on WSD using graph-based approaches (unsupervised methods). Three algorithms were tested and compared to the state of the art. Results indicate that similar performance could be reached with different levels of complexity, what may point to a new approach to this problem.

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