The Role of Subjectivity in Intelligent Systems Communication and
... In activity theory perspective agency is a negotiated relationship between the subject and
the tools used by a subject to perform activities in a determinate context (Bernat 2011).
Seen from the activity theory perspective Agency is in fact liable to change in response to
new contextual development ...
Eliciting Single-Peaked Preferences Using Comparison Queries
... Rather, the agents’ preferences, or at least the relevant parts thereof, need to be elicited.
This is done by asking the agents a (hopefully small) number of simple queries about
their preferences, such as comparison queries, which ask an agent to compare two of the
alternatives. Prior work on prefe ...
Towards Smart User Models for Open Environments
... to re-use user models acquired from a domain to another one. For example, if it is learn
that the user prefers comfortable restaurants, it is quite hard to take advantages of such
information in order to recommend to the user comfortable cinemas.
Second, the influence that the context has in the use ...
... (iii) that EPCs may reproduce stereotypes from everyday real life human-human
interaction, as well as from traditional visual media – but that they simultaneously harbour a considerable potential to challenge stereotypes.
As a tool for the research community, a framework of a visual graphical design ...
... Does this mean that the
internal mechanisms are
complex as well?
No, path results from the interaction between
the ant and the beach
Internal mechanisms are simple
Multiagent Systems : A Modern Approach to Distributed Artificial
... needed is a book that offers a comprehensive and up-to-date introduction and is suitable as a textbook for the field. The
purpose of this volume is to fulfill this need.
Features — The book offers a number of features that make it especially useful to readers:
Cooperative Heuristic Search with Software Agents - Aalto
... complex systems and processes in the natural world, and yet our computations
must fit this parallel model of independence or minimized communication. It is
true that at the hardware level instruction execution is linearized per functional
unit, but at the same time more and more of these independent ...
Artificial Societies of Intelligent Agents
... 1.4. About BBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2. Artificial Societies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Introduction to Complex Sys ...
Software Performance Estimation Methods for System
... Embedded systems are ubiquitous in our everyday lives, spanning all aspects of
modern life. They appear in small portable devices, such as MP3 players and cell
phones, and also in large machines, such as cars, aircrafts and medical equipments.
Driven by market needs, the demand for new features in e ...
teză de doctorat - AI-MAS
... Faculty of Computer Science and Automatic Control
Artificial Emotion Simulation Techniques for Intelligent Virtual
by M.Sc. Valentin Lungu
Pdf - Text of NPTEL IIT Video Lectures
... Then, if you look at the aspect of determinism again environments can be divided into
two or three types.
Deterministic environments: In deterministic environments the next state of the
environment is completely described by the current state and the agent’s action. When we
looked at diagram of agen ...
... Apply genetic algorithms to combinatorial optimization problems.
CptS 440 / 540 Artificial Intelligence
... Why Study AI?
• AI makes computers more useful
• Intelligent computer would have huge impact
• AI cited as “field I would most like to be in” by
scientists in all fields
• Computer is a good metaphor for talking and
thinking about intelligence
Enactive Artificial Intelligence
... itself as a viable methodology for synthesizing and understanding cognition (e.g.
Pfeifer & Bongard 2007; Pfeifer & Scheier 1999). Furthermore, embodied AI is now
widely considered to avoid or successfully address many of the fundamental problems
encountered by traditional “Good Old-Fashioned AI” (H ...
What is AI?
... AI discovers computational complexity
Neural network research almost disappears
Early development of knowledge-based systems
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents
CS 331: Dr M M Awais (LUMS)
Basic Marketing, 16e
... intelligent agents that can work
independently and also together to
perform a task
types of anticipatory behaving agents in artificial life
... The first example is taken from work (Nadin 2003).
Change in posture (standing up from a seated position
for example) would cause changes in blood pressure.
This is the physics of the body consisting from a liquid
(blood), pipes (the various blood vessels), and a pump
(the heart). We can understand ...
Management Information Systems 6/e
... intelligence (AI) – the science of making
machines imitate human thinking and behavior
Robot – a mechanical device equipped with
simulated human senses and the ability to take action
on its own
GNU/Linux AI & Alife HOWTO
... clasp is an answer set solver for (extended) normal logic programs. It combines the high-level
modeling capacities of answer set programming (ASP) with state-of-the-art techniques from the area
of Boolean constraint solving. The primary clasp algorithm relies on conflict-driven nogood learning,
a te ...
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.