Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!eecae!netnews.upenn.edu!rutgers!att!whuts!homxb!antique!abg From: abg@antique.UUCP (Allen Ginsberg) Newsgroups: comp.ai Subject: Call for Papers Message-ID: <2549@antique.UUCP> Date: 10 Feb 89 16:33:41 GMT Organization: AT&T Bell Labs, Holmdel, NJ Lines: 146 For some reason this posting did not reach our site. I am reposting it in case if failed to reach others as well. A. Ginsberg ******************************************************************************* _C_a_l_l _f_o_r _P_a_p_e_r_s: _S_i_x_t_h _I_n_t_e_r_n_a_t_i_o_n_a_l _W_o_r_k_s_h_o_p _o_n _M_a_c_h_i_n_e _L_e_a_r_n_i_n_g Cornell University Ithaca, New York; U.S.A. June 29 - July 1, 1989 The Sixth International Workshop on Machine Learning will be held at Cornell University from June 29 through July 1, 1989. The workshop will be divided into six parallel sessions, each focusing on a different theme: Combining Empirical and Explanation-Based Learning (_M. _P_a_z_z_a_n_i, _c_h_a_i_r). Both empirical evaluation and theoretical analysis have been used to identify the strengths and weaknesses of individual learning methods. Integrated approaches to learning have the potential of overcoming the limitations of individual methods. Papers are solicited exploring hybrid techniques involving, for example, explanation-based learning, case-based reasoning, constructive induction, or neural networks. Empirical Learning; Theory and Application (_C. _S_a_m_m_u_t, _c_h_a_i_r). This session will be devoted to discussions on inductive (also called empirical) learning with particular emphasis on results that can be justified by theory or experimental evaluation. Papers should characterize methodologies (either formally or experimentally), their performance and/or problems for which they are well/ill suited. Comparative studies applying different methodologies to the same problem are also invited. Learning Plan Knowledge (_S. _C_h_i_e_n _a_n_d _G. _D_e_J_o_n_g, _c_o-_c_h_a_i_r_s). This session will explore machine learning of plan-related knowledge; specifically, learning to construct, index, and recognize plans by using explanation-based, empirical, case- based, analogical, and connectionist approaches. Knowledge-Base Refinement and Theory Revision (_A. _G_i_n_s_b_e_r_g, _c_h_a_i_r). Knowledge-base refinement involves the discovery of plausible refinements to a knowledge base in order to improve the breadth and accuracy of the associated expert system. More generally, theory revision is concerned with systems that start out having some domain theory, but one that is incomplete and fallible. Two basic problems are how to use an imperfect theory to guide one in learning more about the domain as more experience accumulates, and how to use the knowledge so gained to revise the theory in appropriate ways. Incremental Learning (_D. _F_i_s_h_e_r, _c_h_a_i_r, _w_i_t_h _J. _G_r_e_f_e_n_s_t_e_t_t_e, _J. _S_c_h_l_i_m_m_e_r, _R. _S_u_t_t_o_n, _a_n_d _P. _U_t_g_o_f_f). Incremental learning requires continuous adaptation to the environment subject to performance constraints of timely response, environmental assumptions such as noise or concept drift, and knowledge base limitations. Papers that cross traditionally disparate paradigms are highly encouraged, notably rule-based, connectionist, and genetic learning; explanation-based and inductive learning; procedure and concept learning; psychological and computational theories of learning; and belief revision, bounded rationality, and learning. Representational Issues in Machine Learning (_D. _S_u_b_r_a_m_a_n_i_a_n, _c_h_a_i_r). This session will study representational practice in machine learning in order to understand the relationship between inference (inductive and deductive) and choice of representation. Present-day learners depend on careful vocabulary engineering for their success. What is the nature of the contribution representation makes to learning, and how can we make learners design/redesign hypotheses languages automatically? Papers are solicited in areas including, but not limited to, bias, representation change and reformulation, and knowledge-level analysis of learning algorithms. _P_A_R_T_I_C_I_P_A_T_I_O_N Each workshop session is limited to between 30 and 50 participants. In order to meet this size constraint, attendance at the workshop is by invitation only. If you are active in machine learning and you are interested in receiving an invitation, we encourage you to submit a short vita (including relevant publications) and a one-page _r_e_s_e_a_r_c_h _s_u_m_m_a_r_y describing your recent work. Researchers interested in presenting their work at one of the sessions should submit an _e_x_t_e_n_d_e_d _a_b_s_t_r_a_c_t (4 page maximum) or a _d_r_a_f_t _p_a_p_e_r (12 page maximum) describing their recent work in the area. Final papers will be included in the workshop proceedings, which will be distributed to all participants. _S_U_B_M_I_S_S_I_O_N _R_E_Q_U_I_R_E_M_E_N_T_S Each submission (research summary, extended abstract, or draft paper) must be clearly marked with the author's name, affiliation, telephone number and Internet address. In addition, you should clearly indicate for which workshop session your submission is intended. Deadline for submission is March 1, 1989. Submissions should be mailed directly to: 6th International Workshop on Machine Learning Alberto Segre, Workshop Chair Department of Computer Science Upson Hall Cornell University Ithaca, NY 14853-7501 USA Telephone: (607) 255-9196 Internet: ml89@cs.cornell.edu While hardcopy submissions are preferred, electronic submissions will be accepted in TROFF (me or ms macros), LaTeX or plain TeX. Electronic submissions must consist of a single file. Be sure to include all necessary macros; _i_t _i_s _t_h_e _r_e_s_p_o_n_s_i_b_i_l_i_t_y _o_f _t_h_e _s_u_b_m_i_t_t_e_r _t_o _e_n_s_u_r_e _h_i_s/_h_e_r _p_a_p_e_r _i_s _p_r_i_n_t_a_b_l_e _w_i_t_h_o_u_t _s_p_e_c_i_a_l _h_a_n_d_l_i_n_g. Foreign contributors may make special arrangements on an individual basis for sending their submissions via FAX. Submissions will be reviewed by the individual session chair(s). Determinations will be made by April 1. Attendance at the workshop is by invitation only; you must submit a paper, abstract or research summary in order to be considered. While you may make submissions to more than one workshop session, each participant will be invited to only one session. _I_M_P_O_R_T_A_N_T _D_A_T_E_S March 1, 1989 Submission deadline for research summaries, extended abstracts and draft papers. April 1, 1989 Invitations issued; presenters notified of acceptance. April 20, 1989 Final camera-ready copy of accepted papers due for inclusion in proceedings.