Modeling Student Learning: Binary or Continuous Skill?
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
Chapter 8 Multi
... the rule is to fire; and an action part, which states what is to happen when the rule fires. For example, a robot agent might include the rule ‘if (a) your arm is raised and (b) the goal is to pick up an object and (c) an object is on the table, then lower your arm’. This would be one of perhaps hun ...
... the rule is to fire; and an action part, which states what is to happen when the rule fires. For example, a robot agent might include the rule ‘if (a) your arm is raised and (b) the goal is to pick up an object and (c) an object is on the table, then lower your arm’. This would be one of perhaps hun ...
The Evolutionary Emergence of Socially Intelligent Agents
... artificial selection to evolve complex behaviors. However, artificial selection has kept its hold so far − most systems still use fitness functions. Much of this work is based on the 'Red Queen' or 'Arms Race' phenomenon (see Cliff and Miller, 1995; Dawkins and Krebs, 1979), an early example of whic ...
... artificial selection to evolve complex behaviors. However, artificial selection has kept its hold so far − most systems still use fitness functions. Much of this work is based on the 'Red Queen' or 'Arms Race' phenomenon (see Cliff and Miller, 1995; Dawkins and Krebs, 1979), an early example of whic ...
Two Paradigms Are Better Than One, And Multiple
... whose intelligence is at the level of ants. A society of such agents can cooperate in defending the colony, searching for food, and caring for the eggs and larvae. But no one has shown how a colony of ants could understand language or do complex reasoning and planning. Complex rational agents and s ...
... whose intelligence is at the level of ants. A society of such agents can cooperate in defending the colony, searching for food, and caring for the eggs and larvae. But no one has shown how a colony of ants could understand language or do complex reasoning and planning. Complex rational agents and s ...
Lecture 2: Intelligent Agents
... • A percept is a complete set of readings from all of the agent’s sensors at an instant in time • For the robot vacuum cleaner, this will consist of its location and whether the floor is clean or dirty • Example percept: [A, dirty] • A percept sequence is a complete ordered list of the percepts that ...
... • A percept is a complete set of readings from all of the agent’s sensors at an instant in time • For the robot vacuum cleaner, this will consist of its location and whether the floor is clean or dirty • Example percept: [A, dirty] • A percept sequence is a complete ordered list of the percepts that ...
Junior CFP - IEEE SMC 2017
... conference of the IEEE Systems, Man, and Cybernetics Society the SMC Junior 2017 will be organized again this year. It provides an international forum for student and young researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange idea ...
... conference of the IEEE Systems, Man, and Cybernetics Society the SMC Junior 2017 will be organized again this year. It provides an international forum for student and young researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange idea ...
document
... environment via sensors and acts rationally upon that environment with its acutators. Hence, an agent gets percepts one at a time, and maps this percept sequence to actions. ...
... environment via sensors and acts rationally upon that environment with its acutators. Hence, an agent gets percepts one at a time, and maps this percept sequence to actions. ...
Artificial Intelligence Intelligent Autonomous Agents 1
... percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. ...
... percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. ...
Assigned Resources Trained Artificial intelligence
... judgment. If the activity duration is deterministic, the CPM can be a very useful tool in managing project schedule. However, in practice, uncertainties in the project environment and variation in the quality of resources cause activity duration to become indeterministic. In projects, in general, th ...
... judgment. If the activity duration is deterministic, the CPM can be a very useful tool in managing project schedule. However, in practice, uncertainties in the project environment and variation in the quality of resources cause activity duration to become indeterministic. In projects, in general, th ...
Editorial: Agency in Natural and Artificial Systems
... can only be done by another agent: our claim is that we have not generated real agency yet —but how will we know we have obtained our goal? In answering this question, there is a tension between criteria that focus on the generative mechanisms and those that focus on the appeal of surface behavior. ...
... can only be done by another agent: our claim is that we have not generated real agency yet —but how will we know we have obtained our goal? In answering this question, there is a tension between criteria that focus on the generative mechanisms and those that focus on the appeal of surface behavior. ...
Lecture Notes in Computer Science
... In the Society of Mind, and in subsequent work [20], Minsky defined a variety of types of agents, each involved in the management, selection, admission and censorship of other agents who are selfishly trying to express themselves and exploit others. The theory was relatively high level and served as ...
... In the Society of Mind, and in subsequent work [20], Minsky defined a variety of types of agents, each involved in the management, selection, admission and censorship of other agents who are selfishly trying to express themselves and exploit others. The theory was relatively high level and served as ...
A Structural Functionalist Conception of Artificial Agent
... Structural functionalism provides a model of how society, or social structure, “moulds” individual behaviour so as to achieve globally functional behaviour. Still, in order to use this model as a metaphor in the design of multiagent systems, we need to turn structural functionalist analysis upside d ...
... Structural functionalism provides a model of how society, or social structure, “moulds” individual behaviour so as to achieve globally functional behaviour. Still, in order to use this model as a metaphor in the design of multiagent systems, we need to turn structural functionalist analysis upside d ...
Agent and Environment - Computer Science and Engineering
... Cognitive Science and Psychology (testing/ predicting responses of human subjects) Cognitive Neuroscience (observing neurological data) ...
... Cognitive Science and Psychology (testing/ predicting responses of human subjects) Cognitive Neuroscience (observing neurological data) ...
Mechanism Design for Computationally Limited Agents
... • Mechanism: M=(S1,…,Sn,g(.)) is a set of strategies Si available to each agent i, and an outcome function g(s). – Defines strategies available to agents – Describes methods used to determine outcome based on agents’ strategies ...
... • Mechanism: M=(S1,…,Sn,g(.)) is a set of strategies Si available to each agent i, and an outcome function g(s). – Defines strategies available to agents – Describes methods used to determine outcome based on agents’ strategies ...
The 19th International Workshop on Principles of Diagnosis (DX-08)
... Several conferences are to be held in Australia within a month: CP, International Conference on Principles and Practice of Constraint Programming; ICAPS, International Conference on Automated Planning and Scheduling; KR, International Conference on Principles of Knowledge Representation and Reasonin ...
... Several conferences are to be held in Australia within a month: CP, International Conference on Principles and Practice of Constraint Programming; ICAPS, International Conference on Automated Planning and Scheduling; KR, International Conference on Principles of Knowledge Representation and Reasonin ...
here
... ability to make sense of, and predict what is happening in an environment—in disaster management, military reconnaissance, space exploration, and climate research. In these domains, and many others besides, their use reduces the need for exposing humans to hostile, impassable or polluted environment ...
... ability to make sense of, and predict what is happening in an environment—in disaster management, military reconnaissance, space exploration, and climate research. In these domains, and many others besides, their use reduces the need for exposing humans to hostile, impassable or polluted environment ...
Emotional Intelligence – Applications Based on Multi
... specification of how emotions should be implemented in an artificial agent. ...
... specification of how emotions should be implemented in an artificial agent. ...
Special issue: Computational intelligence models for image
... Abstract. Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabil ...
... Abstract. Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabil ...
Proceedings 1998 International Conference on Web
... to release a product in which they are confident and that is accepted by clients. They need the participation of the clients to get the needed understanding. The clients participation is needed also during all phases of the design process. This participation will facilitate the understanding and app ...
... to release a product in which they are confident and that is accepted by clients. They need the participation of the clients to get the needed understanding. The clients participation is needed also during all phases of the design process. This participation will facilitate the understanding and app ...
Carving Out Evolutionary Paths Towards Greater Complexity
... modular morphology, such that sensors and actuators have a finite number of areas they can be attached at, producing morphological designs based on setups we know work (quadropods, bipeds...). In this manner, we can evolve an infomorph whose morphology can also evolve with it over time in some simul ...
... modular morphology, such that sensors and actuators have a finite number of areas they can be attached at, producing morphological designs based on setups we know work (quadropods, bipeds...). In this manner, we can evolve an infomorph whose morphology can also evolve with it over time in some simul ...
Knowledge-based agents
... makes him selecting similar choices to reinforce his happiness. E.g. anger or distress may guide an agent into selecting choices he would otherwise not consider. Those choices maybe the solution to his problem. A thought: A* is an optimistic algorithm. Because of that it is complete (always finds a ...
... makes him selecting similar choices to reinforce his happiness. E.g. anger or distress may guide an agent into selecting choices he would otherwise not consider. Those choices maybe the solution to his problem. A thought: A* is an optimistic algorithm. Because of that it is complete (always finds a ...