By M. L. Barrón-Estrada, Ramón Zatarain-Cabada, Rosalío Zatarain-Cabada (auth.), Angel Kuri-Morales, Guillermo R. Simari (eds.)
This e-book constitutes the refereed complaints of the twelfth Ibero-American convention on man made Intelligence, IBERAMIA 2010, held in Bahía Blanca, Argentina, in November 2010. The sixty one papers provided have been rigorously reviewed and chosen from 148 submissions. The papers are prepared in topical sections on man made intelligence in schooling, cognitive modeling and human reasoning, constraint delight, evolutionary computation, details, integration and extraction, wisdom acquisition and ontologies, wisdom illustration and reasoning, computing device studying and information mining, multiagent platforms, typical language processing, neural networks, making plans and scheduling, probabilistic reasoning, seek, and semantic internet.
Read or Download Advances in Artificial Intelligence – IBERAMIA 2010: 12th Ibero-American Conference on AI, Bahía Blanca, Argentina, November 1-5, 2010. Proceedings PDF
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A partial specification of the corresponding dialogue model is shown in Figure 3. In this model there is a situation si-1 in which the system makes the offer βi-1 = “offer(a, b)” (where a and b are the posters that can be explained) and moves to situation si. e. a propositional function); in the case this expectation is satisfied, the system needs to produce the corresponding explanation, so βi = “explain(x)”. a) b) Fig. 3. A functional dialogue model Suppose now that in an actual interaction the user asserts that he or she wants to see poster a at situation si; then, the expectation “user wants to see a” is satisfied, the action “explain(a)” is performed and the system moves to situation si+1, to continue with the interaction.
Meza, and L. Salinas have as their main argument the interaction’s history. Whenever the dialogue manager reaches a situation that has a functional specification, the functions are evaluated first and the links are interpreted using the corresponding functions’ values. Also, we have introduced a notation for the functional definition of the next situation. e. its possible next situations) by dashed-links, as illustrated in Figure 2. Fig. 2. Functional representation of expectations, actions and transitions Consider an application in which a robot is the guide of a poster session.
In this case the expectation has the form α = “assert(name(x))”. Free variables within the body of expectations need to be assigned values from the world through perception, providing an indexical interpretation component to the system. The user may provide his or her name through spoken or written text, and he or she may use different expressions for this purpose, like “Peter”, “my name is Peter”, “hum… shall I tell you my name? Ok, my name is Peter”, etc. However, in all these scenarios the overall purpose of the perceptual component is to assign the value “Peter” to the variable x, in order to satisfy the predicate “assert(name(Peter))”.
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