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Reasoning about Topological and Positional Information in Dynamic Settings
Reasoning about Topological and Positional Information in Dynamic Settings

... connected in the northeast to a third one. Although topological and positional information are usually assumed to be orthogonal, we will observe that in the presented formalism positional relations between two or more objects will restrict the set of possible topological relations between these obje ...
References - Center for the Ecological Study of Perception and Action
References - Center for the Ecological Study of Perception and Action

... cognition: The primacy of action, intention and emotion (pp. 173-190). Thorverston, UK: Imprint Academic. Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, MA: MIT Press (Chapter 3: Semantics). James, W. (1907/1975). Pragmatism and the meaning of truth. Cambridge, MA: Cambridg ...
Business Intelligence: Optimization for Decision Making
Business Intelligence: Optimization for Decision Making

... direct search method, i.e. gradient free individuals follow very simple behaviors: emulate the success of neighboring individuals, but also bias towards on experience of success ...
Exploring the Complex Interplay between AI and Consciousness
Exploring the Complex Interplay between AI and Consciousness

... Global Workspace Theory and, hence, of functional consciousness. Knowledge sources in the blackboard architecture correspond to processors in GWT. The blackboard’s control shell plays the role of attention, the competition for consciousness in GWT. Writing to be blackboard corresponds to the global ...
Chapter 1 ARGUMENTATION THEORY AND DECISION AIDING
Chapter 1 ARGUMENTATION THEORY AND DECISION AIDING

... “that action” is the best one (we are not going to discuss the rationality hypotheses about “best” here). Decision Theory and Multiple Criteria Decision Analysis have focussed on such issues for a long time, but more on how this “best solution” should be established and less on how a decision maker ...
Self-Motivating Computational System Cognitive Architecture
Self-Motivating Computational System Cognitive Architecture

... The road to building artificial general intelligence (AGI) [9] is not just very complex but the most complex task computer scientists have tried to solve. While over the last 30+ years a great amount of work has been done, much of that work has been narrow from an application standpoint or has been ...
The Unconscious Mind as a Means for Authentication - E
The Unconscious Mind as a Means for Authentication - E

Enhancing SAT Based Planning with Landmark Knowledge
Enhancing SAT Based Planning with Landmark Knowledge

... In this section we introduce the concept of landmarks and their SAT encoding. Landmarks were first defined by Porteous et al. [7] as “facts that must be true at some point in every valid solution plan”. Later, Richter [8] extended the definition by changing ‘facts’ to the more inclusive ‘proposition ...
FLP Semantics Without Circular Justifications for General Logic
FLP Semantics Without Circular Justifications for General Logic

... Ai is an atom. Informally, given an interpretation I the FLP reduct of Π w.r.t. I, denoted f ΠI , consists of all ground rules of Π whose bodies are satisfied by I. Then, I is an FLP answer set of Π if I is a minimal model of f ΠI . Observe that the FLP reduct can easily be extended to more general ...
Reinforcement Learning and Automated Planning
Reinforcement Learning and Automated Planning

... sophisticated reasoning capabilities which should be simulated by the software system. Therefore, Planning Systems make extensive use of Artificial Intelligence techniques and there is a dedicated area of AI called Automated Planning. Automated Planning has been an active research topic for almost 4 ...
Computational Models of Emotion and Cognition
Computational Models of Emotion and Cognition

... Mood An overall state of emotion which is sustained over longer periods of time and is less changeable than emotions themselves Computational models of emotion and cognition which we address are those that try to explain emotion in the context of its intimate relationship with cognition. These model ...
Oxford University Press
Oxford University Press

... that does not make use of computers! The use of computers has become so widespread that almost every electrical and electronic device (such as a washing machine or an air conditioner) has a small embedded computer within it. In India, computers are now a part of millions of households. Even the mobi ...
Robotics and Artificial Intelligence: a Perspective on
Robotics and Artificial Intelligence: a Perspective on

... Initial and goal configurations are added to this graph, between which a path is computed. This path is then transformed into a smooth trajectory. Manipulation planning requires finding feasible sequences of grasping positions, each of which is a partial constraint on the robot configuration that ch ...
Intelligent Agents
Intelligent Agents

... Chief among them is the ability of an agent to react to changes in the environment and to exploit opportunities when they arise.  If an agent is pushing a tile to fill a hole and the hole disappears before being filled, the agent should realize its original goal is no longer in effect and ‘rethink’ ...
The analog/digital distinction in the philosophy of mind
The analog/digital distinction in the philosophy of mind

Dr. Person′s Curriculum Vitae
Dr. Person′s Curriculum Vitae

... B., & The Tutoring Research Group (2001). AutoTutor: An Intelligent Tutor and Conversational Tutoring Scaffold. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.) Artificial intelligence in education: AI-ED in the wired and wireless future (pp. 47-49). Amsterdam, IOS Press. Graesser, A.C., Perso ...
Learning to Plan in Complex Stochastic Domains
Learning to Plan in Complex Stochastic Domains

... are Goal-Directed MDPs, whose execution terminates when the agent reaches a terminal or goal state. We treat the problem of an agent operating in an Goal-Directed MDP as equivalent to the probabilistic planning problem. Computing optimal solutions to MDPs is known to be P-Complete with respect to th ...
Against the Moral Turing Test - Human
Against the Moral Turing Test - Human

... reproduce, not just imitate, thought. If one’s responses pick up concepts correctly and apply them in a non-repetitive and adaptive manner, with fidelity to current circumstances and without clear prior scripting, there seems less and less ground to deny that thought is occurring. This is not just b ...
A distributed problem-solving approach to rule induction
A distributed problem-solving approach to rule induction

... Incorporating flexibility and the dynamic performance of the agents in an organization. This school of thought has led to the recent emphasis on learning in organizational decision making(Ching et al.[ 1990|). Coordination is necessary in DAI systems for resolving conflicts, allocating limited resou ...
5 Ways Artificial Intelligence is Transforming and Automating
5 Ways Artificial Intelligence is Transforming and Automating

... Intelligent Self-Learning Smarter over Time Excellent at Repetitive Tasks ...
FraMoTEC: A Framework for Modular Task-Environment
FraMoTEC: A Framework for Modular Task-Environment

... This begs the question on how can we assess whether or not someone (— or something) is capable of doing some task — one way would be to simply put the individual to the test, a potentially risky approach (imagine tasks like nuclear safety inspection or firefighting). We could instead test the indivi ...
Parameterized Splitting: A Simple Modification
Parameterized Splitting: A Simple Modification

... This work has been funded by Vienna Science and Technology Fund (WWTF) through project ICT08-028. ...
Procedural Knowledge Representations
Procedural Knowledge Representations

Representation = Grounded Information - Our research
Representation = Grounded Information - Our research

... breakthroughs. Furthermore, whilst his robot designs were not based on logical representations they were not devoid of symbolic representation e.g. they used entities in computer memory such as numbers. In this paper we put forward the proposal that representations are grounded information, and that ...
The Chinese Room Argument
The Chinese Room Argument

... Does computer prowess at challenging games and conversation then show that computers can understand and be intelligent? Will further development result in digital computers that fully match or even exceed human intelligence? Alan Turing (1950), one of the pioneer theoreticians of computing, believed ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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