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Rational Artificial Intelligence for the Greater Good - Self
Rational Artificial Intelligence for the Greater Good - Self

... will respond intelligently to changing circumstances and adapt to novel environmental conditions. Their decision-making powers may help us solve many of today’s problems in health, finance, manufacturing, environment, and energy [7]. These technologies are likely to become deeply integrated into our ...
Introduction to Cognitive Science
Introduction to Cognitive Science

... A good model is complete (does not abstract out important properties) and faithful (does not introduce features that are not in the original) with respect to its specific purpose. Helpful for understanding a complex system – cognition for the case of cognitive science. Computational cognitive modell ...
Reduction Considered Harmful
Reduction Considered Harmful

... in each. The Model is "context free". You yourself must provide the analysis of the context. If you have cartons of eggs, you must Understand that the cartons are containers and the eggs are items, not the other way around. You must simplify – "Reduce" – the real life situation in your mind so that ...
this PDF file
this PDF file

... Testbeds reported in the literature do not provide for multiple types of agents with each potentially using a different AI method, whereas the proposed solution does provide these features. The eventual goal of the proposed solution is to compare the effects of many AI methods among members of an ag ...
Artificial life - The University of Texas at Dallas
Artificial life - The University of Texas at Dallas

... centralized controller that makes decisions based on access to all aspects of global state. The controller’s decisions have the potential to affect directly any aspect of the whole system. On the other hand, many natural living systems exhibiting complex autonomous behavior are parallel, distributed ...
Reports of the AAAI 2008 Spring Symposia
Reports of the AAAI 2008 Spring Symposia

... fundamental issues in affect and personality in both biological and artificial agents, focusing on the roles of these factors in mediating social behavior. The Semantic Scientific Knowledge Integration symposium brought together the semantic technologies community with the scientific information tec ...
Demystifying Emergence - CSIRO Marine Research (Perth)
Demystifying Emergence - CSIRO Marine Research (Perth)

... In this short presentation, we summarize key outcomes of our 3-year Interaction Task on Emergence. For the most part, we confined our discussions to three issues: • Relationship(s) between emergence, evolution, self-organisation and complexity; • Relationship(s) between emergence and formal logic, c ...
Exploiting Anonymity and Homogeneity in Factored Dec
Exploiting Anonymity and Homogeneity in Factored Dec

session02
session02

... • Agents have social ability, that is, they communicate with the user, the system, and other agents as required • Agents may also cooperate with other agents to carry out more complex tasks than they themselves can handle • Agents may migrate from one system to another to access remote resources or ...
Experimental and causal view on information integration in
Experimental and causal view on information integration in

... Problem instance: navigation from video in ‘Malmo’ Background: AI experimentation platform ‘Malmo’: library for programming agents for ‘Minecraft’ (computer game) [Bignell2016] Task: unknown landscape; navigate to visually recognizable goal Available heterogeneous information: I agent’s own sensors ...
agent function
agent function

Case-based Reasoning and Multiple-agent Systems for Accounting
Case-based Reasoning and Multiple-agent Systems for Accounting

... Abstract One of the areas of judgment research in accounting and financial applications is that of accounting regulation. Previously, artificial intelligence efforts at modeling human judgment in accounting regulation systems have concentrated on rule-based expert systems. In those systems, genera ...
Agent Computing and Situation Aware
Agent Computing and Situation Aware

... Within the framework, complex processes are designed as compositional architectures consisting of interacting task-based hierarchically structured components. The interaction between components, and between components and the external world is explicitly specified. Components can be primitive reason ...
using simulation and neural networks to develop a scheduling advisor
using simulation and neural networks to develop a scheduling advisor

... 4.2 Data Collection: Determining the Optimal Inputs in Small Scale Job Shop Problems In order to construct a neural network-scheduling advisor to indicate the position of the job in the queue that must be processed the neural network should be trained using a data set of decisions with the associate ...
An Equal Excess Negotiation Algorithm for Coalition
An Equal Excess Negotiation Algorithm for Coalition

... the accrued total revenue, we found that the PACT solutions were within 3% of the utilitarian solutions. This suggests that the tasks that PACT allocates are mostly identical to those selected by the utilitarian solution. Our second set of experiments test the scalability of the PACT algorithm. We ...
Applying Complex Adaptive Systems to Actuarial Problems
Applying Complex Adaptive Systems to Actuarial Problems

View PDF - Advances in Cognitive Systems
View PDF - Advances in Cognitive Systems

... • Divide the problem into well-defined pieces • Make progress on each one • Build bridges to create a unified whole The problem with this model is that the individual solutions may be too far apart (as Koller herself points out), and not stable enough, to support bridges. For example, most work in c ...
Programmability of Intelligent Agent Avatars (Extended Abstract)
Programmability of Intelligent Agent Avatars (Extended Abstract)

... result in a set of intentions, more exactly, a set of intended actions. By acting, avatars would use their effectors to take the intended actions. In the current version of WASP soccer games, we do not require that agents would know all the laws of soccer games. The agents in the WASP soccer game us ...
Approaches to Artificial Intelligence
Approaches to Artificial Intelligence

... algorithms to run on parallel hardware, developing new selective search algorithms that focus their attention on the most important parts of the search space, and search algorithms for online problems where decisions are required in real time. The research methodology is typically to develop new alg ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • Agents have social ability, that is, they communicate with the user, the system, and other agents as required • Agents may also cooperate with other agents to carry out more complex tasks than they themselves can handle • Agents may migrate from one system to another to access remote resources or ...
An Investigation of the Distributional Characteristics of Generative
An Investigation of the Distributional Characteristics of Generative

... both. There has been little research focusing on the connections between local and global properties and the accuracy/efficiency tradeoffs between matching on one or the other. Moreover, analysis of these proposed methods has generally centered on empirical validation that the properties of interest ...
Behavior-based robotics as a tool for synthesis of artificial behavior
Behavior-based robotics as a tool for synthesis of artificial behavior

... may not have a specific internal ‘flocking’ behavior; instead, its interaction with the environment and other robots may produce flocking. Typically, behavior-based systems are designed so that the effects of the behaviors interact in the environment, rather than internally through the system, so as ...
Intelligent Systems: Perspectives and Research Challenges
Intelligent Systems: Perspectives and Research Challenges

... centralised  architecture  with  perception,  cognition  and  execution  functions  implemented  as  separate  but  interconnected  subsystems.  However,  from  the  engineering point of view a centralised architecture is not feasible. For example,  the complexity of a centralised perception subsyst ...
JRobin - LES - PUC-Rio
JRobin - LES - PUC-Rio

...  Next steps:  Improve reasoning trace GUI  Switch from chronological backtracking to conflict-directed backjumping for disjunctive rule and finite domain labeling search ...
The BICA Cognitive Decathlon
The BICA Cognitive Decathlon

... instructor is performing. Tasks could range from simple object-action events (“I am dropping the ball”) to object construction (“I am building a tower.”), coordinated action (“I am hitting the cup with the hammer.”), and complex compound events. (“I am sweeping the floor”). 3.3 Self-directed Search ...
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Agent-based model

An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S. (""Keep it simple, stupid"") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience ""learning"", adaptation, and reproduction.Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) a non-agent environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.
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