This issue is both a report on and a critical disussion of the collection of papers entitled Mind versus Computer which appeared twice: as a special issue of the journal Informatica in 1995 and as a book edited by M.Gams, M.Paprzycki and X.Wu, IOS Press, Amsterdam, etc, 1997.
Two important contributions included in the former but not in the latter version are reported here too. The collection, having been meant as a brainstorming, is worth special attention. It brings an extensive survey of AI research as well as inspiring ideas.

1. Introduction

2. Were Dreyfus and Winograd Right? This question, used as the subtitle of the volume, is commented on by W. Marciszewski.

A Survey of Mind vs Computer

The following method of reviewing MvC is adopted in this issue. A short synopsis (below) is given. It is identical with the survey provided by the MvC Editors, including their partition of papers into three groups (A, B, C, corresponding to files 3,4,5 in this directory). The title of each group functions as link to the respective file that contains the abstracts of contributions so grouped.

3. Overview and General Issues (A)

A1. "Strong AI": An Adolescent Disorder
by D. Michie -- advocates an integrative approach: let us forget about differences and keep doing interesting things.

A2. AI Progress, Massive Parallelism and Humility
by J.Geller -- An approach to AI based on the combination of Knowledge Representation with Massively Parallel hardware.

A3. Self and Self-Organization in Complex AI Systems
by B.Goertzel -- In order to make strong AI a reality, formal logic and formal neural network theory must be abandoned in favour of complex systems science.

A4. Strong vs. Weak AI
by M. Gams -- presents an overview of the antagonistic approaches and proposes an AI version of the Heisenberg principle delimiting strong from weak AI.

A5. Naive Psychology and Alien Intelligence
by S. Watt -- argues that AI has failed to address the whole problem of common sense; AI must enable us to see computers as minds.

A6. Cramming Mind into Computer: Knowledge and Learning for Intelligent Systems
by K.J. Cherkauer -- analyses knowledge acquisition and learning as the key issues necessary for designing intelligent computers.

A7. The Quest for Meaning
by L. Marinoff -- attacks strong AI through an experiment-based criticism of Turing test.

4. New Approaches (B)

B1. Computation and Embodied Agency
by P.E. Agre -- analyses computational theories of agents' interactions with their environments.

B2. Why Philosophy? On the Importance of Knowledge Representation and its Relation to Modeling Cognition
by M.F. Peschl -- investigates the role of representation in both cognitive modeling and the development of human-computer interfaces.

B3. Intelligent Objects: An Integration of Knowledge, Inference and Objects
by X. Wu, S. Ramakrishnan and H. Dai -- introduces knowledge objects and intelligent objects based on the integration of rules and objects.

B4. Emotion-Based Learning: Building Sentient Survivable Systems
by S. Walczak -- advocates implementing features such as affects in order to design intelligent programming systems.

B5. The Theoretical Foundations for Engineering a Conscious Quantum Computer
by R.L. Amoroso -- treats the brain as a natural quantum computer and suggests a cosmology of consciousness.

B6. Mind: Neural Computing Plus Quantum Consciousness
by M.Perus -- discusses what characteristics future computers would have to possess in order to be treated as mind-like.

B7. Computation without Representation: Nonsymbolic-Analog Processing
by R.S. Stufflebeam -- defends nonsymbolic-analog processing as part of a computational framework to explain how biological intelligent systems work.

5. Computability and Form versus Meaning (C)

C.1. Is Consciousness a Computational Property?
by G. Caplain - proposes a detailed argument to show that mind can not be computationally modeled.

C.2. Computation and the Science of Mind
by P. Schweizer claims that consciousness cannot be adequately described as a computational structure and(or) process.

C.3. Mind versus Gödel
by D.Bojadziev, presents an overview of the uses of Gödel's theorems, claiming that they apply equally to humans and computers.

C.4. Computation und Understanding
by M. Radovan examines various strengths and shortcomings of computers and minds. Although computers in many ways exceed natural mind, brains still have quite a few aces left.

C.5. What Internal Languages Can't Do
by P. Hipwell analyses the limitations of internal representation languages in contrast with the brain's representations.

C.6. The Chinese Room Argument: Consciousness and Understanding
by S. Gozzano proposes yet another reason why Searle's Chinese rooms present a hypothetical situation only.

6. A Debate on Strong AI critically examined.
A comment by W.Marciszewski on the main point of Mind versus Computer, and on two papers contributed to it, one of them arguing for weak AI (see A4 above), the other for strong AI (see A3 above).

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