W.H.E. Davies and P. Edwards. The communication of inductive inferences. In Lecture Notes in Artifical Intelligence (1221): Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, pages 223--241. Springer Verlag, Berlin, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... in a given set of data can be used to compress the data the more regularities there are in the data, the more them can be compressed (see references listed in [Gr u98] and [VL97, LV97] for overviews) reinforcement learning and learning in AI (two incomplete lists are [CB97, Bou96, BGS 91, DE97, Mic93] and [SSH94, Sia91, Wei93, Wei95] Learning, rationality and knowledge As far as we know, a zero one paradigm of coordination was rst introduced in formal learning theory by [MO99b] by using the tools of recursion theory. Kel96] advanced a similar paradigm (see the Frank and ....

W. Davies and P. Edwards. The communication of inductive inferences. In G. Wei, editor, Distributed Articial Intelligence meets Machine Learning, pages 223-241. Springer-Verlag LNAI 1221, 1997.

....but also is rather young and still searching for its defining boundaries and shape. The work described in this article may be considered as an attempt to contribute to this search. Whereas most of the available work on this kind of learning is centered araound large scale inductive learning (e.g. [1, 2, 4, 6]) this articles presents a parallel and distributed learning approach that follows the multiagent learning paradigm known from the field of distributed artificial intelligence (see e.g. 5, 7, 8] for collections of papers describing work on multiagent learning) A problem being well suited for ....

Davies, W., & Edwards, P. (1997). The communication of inductive inferences. In [7].

....to the group performance on the task at hand. In (Mammen Lesser, 1998) the authors investigate the inter related issues of the timing of agent communication and the amount of information which should be communicated. A more robust method of communication of inductive inferences is presented in (Davies Edwards, 1996). The authors suggest including in the communication the context from which the inference was formed, in the form of the version space boundary set. This allows the receiving agents to better integrate the information with the inference it has induced from local information. The methods of ....

Davies, W., & Edwards, P. (1996). The communication of inductive inferences. In Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, New York: SpringerVerlag.

....al. 1996; Plaza et al. 1995) Knowledge sharing tasks Sharing distributed symbolic knowledge Instance Based Learning (Decker and Lesser, 1995; Haynes, 1997; Nagendra Prasad and Lesser, 1997) Decision classification tasks Improving group team behavior (coordination) Inductive Learning (Davies and P. Edwards, 1996; Goldman and Rosenschein, 1995 ; Potter et al. 1995; Stone and Veloso, 1997b) Symbolic classification and knowledge sharing tasks distributed classification strategic learning Sharing confronting distributed symbolic knowledge deciding to be altruistic Genetic Algorithms (Fogarty ....

Davies, W., and P. Edwards. 1996. The Communication of Inductive Inferences. Adaptation and Learning in Multiagent Systems, ed., Weiss, G. and Sen, S., pp. 223-241, Springer Verlag, Berlin.

....to the group performance on the task at hand. In (Mammen Lesser 1998) the authors investigate the inter related issues of the timing of agent communication and the amount of information which should be communicated. A more robust method of communication of inductive inferences is presented in (Davies Edwards 1996). The authors suggest including in the communication the context from which the inference was formed, in the form of the version space boundary set. This allows the receiving agents to better integrate the information with the inference it has induced from local information. The methods of ....

Davies, W., and Edwards, P. 1996. The communication of inductive inferences. In Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, number 1221 in Lecture Notes in Computer Science : Lecture Notes in Artificial Intelligence. New York: Springer-Verlag.

....is now a set of specialised rules and propositions. Our system is thought for the cooperation among agents via the communication of knowledge, not just data, in a similar way to other systems [2] where the communication is about lamdba formulas; or the communication of inductive inferences as in [3], a work on multi agent learning systems. The specialisation calculus is also related to other work on conditioned answers [4, 16, 19] and on the treatment of unknown information [21] It allows us to obtain conditioned answers after the specialisation of a rule base with the known information. ....

Winton Davies and Peter Edwards. The communication of inductive inferences. In Gerhard Weiß, editor, Distributed Artificial Intelligence Meets Machine Learning, volume 1221 of Lecture Notes in Artificial Intelligence, pages 223--241. Springer-Verlag, 1997.

....These include operations such as tell, untell, ask all, ask one, ask if, achieve. The last performative mentioned is to permit planning activity. However, there is no performative to support induction. The final goal of the authors is to provide just such general support. This is work in progress (Davies Edwards, 1997), however, the basic approach is that an agent must send the version space description in order to bound any induced hypothesis. Incremental Learning The first type of learning algorithm we examine, is characterized by ID5 (Utgoff , 1989) ID5 stores information about the examples at each node ....

W. Davies & P. Edwards (1997). The Communication of Inductive Inferences, in Distributed Artificial Intelligence Meets Machine Learning, pages 223-242, Gerhard Weiss (ed.), Springer-Verlag, Berlin, Germany.

....of KQML Performatives for Inductive Inference (2 months) The final goal of this proposal is to establish a set of KQML performatives that will allow multi agent systems to communicate inductive inferences safely and efficiently. A preliminary set of performatives have already been established in (Davies, 1997). In order to demonstrate their utility, and to refine them, an agent based data mining system will be constructed. Using the relational algorithm proposed above, we will show how agents can generate local relational hypotheses, and then communicate and combine them safely and efficiently. The ....

W. Davies & P. Edwards (1997). The Communication of Inductive Inferences, in Distributed Artificial Intelligence Meets Machine Learning, pages 223-242, Gerhard Weiss (ed.), Springer-Verlag, Berlin, Germany.

No context found.

W.H.E. Davies and P. Edwards. The communication of inductive inferences. In Lecture Notes in Artifical Intelligence (1221): Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, pages 223--241. Springer Verlag, Berlin, 1997.

No context found.

W.H.E. Davies and P. Edwards. The communication of inductive inferences. In Lecture Notes in Artifical Intelligence (1221): Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, pages 223-- 241. Springer Verlag, Berlin, 1997.

No context found.

Davies, W., & Edwards, P. (1997). The communication of inductive inferences. In [10].

No context found.

Davies, W., & Edwards, P. (1997). The communication of inductive inferences. In [7].

No context found.

Winton Davies, Peter Edwards, The communication of inductive inferences, in: Gerhard Wei (Ed.). Distributed Artificial Intelligence Meets Machine Learning, vol. 1221 of Lecture Notes in Artificial Intelligence, Springer, Berlin, 1997, pp. 223 241.

No context found.

Davies, W., & Edwards, P. (1996). The communication of inductive inferences. In G. Weiss (Ed.), Distributed Artificial Intelligence Meets Machine Learning (pp. 223-241). Berlin: Springer Verlag.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC