1. Leon Sterling and Ehud Y. Shapiro. The Art of Prolog - Advanced Programming Techniques, 2nd Ed.. MIT Press, 1994.
   Keywords: agentlab.
   

   @Book{Sterling1994,
   author = "Leon Sterling and Ehud Y. Shapiro",
   title = "The Art of Prolog - Advanced Programming Techniques, 2nd Ed.",
   publisher = "MIT Press",
   keywords = "agentlab",
   year = "1994",
   
   }
   




2. Ralph D. Sunzi and Mei-Chün Sawyer. The art of war = [Sun-tzu ping fa], History and warfare.. Westview Press, Boulder, 1994.
   Note: 93047372 Sun-tzu ; translated, with introductions and commentary by Ralph D. Sawyer ; with the collaboration of Mei-Chün Lee Sawyer. Parallel title in Chinese characters. Includes bibliographical references and index. The Art of War in Translation. 1. Initial Estimations. 2. Waging War. 3. Planning Offensives. 4. Military Disposition. 5. Strategic Military Power. 6. Vacuity and Substance. 7. Military Combat. 8. Nine Changes. 9. Maneuvering the Army. 10. Configurations of Terrain. 11. Nine Terrains. 12. Incendiary Attacks. 13. Employing Spies -- Tomb Texts and Lost Writings -- Notes to the General Introduction and Historical Background -- Selected Notes to the Introduction -- Notes to the Translation -- Notes to the Tomb Texts and Lost Writings.
   Keywords: intentionality, philosophy, honours reading.
   

   @Book{Sunzi1994,
   author = "Ralph D. Sunzi and Mei-Chün Sawyer",
   title = "The art of war = [Sun-tzu ping fa]",
   publisher = "Westview Press",
   address = "Boulder",
   series = "History and warfare.",
   note = "93047372 Sun-tzu ; translated, with introductions and commentary by Ralph D. Sawyer ; with the collaboration of Mei-Chün Lee Sawyer. Parallel title in Chinese characters. Includes bibliographical references and index. The Art of War in Translation. 1. Initial Estimations. 2. Waging War. 3. Planning Offensives. 4. Military Disposition. 5. Strategic Military Power. 6. Vacuity and Substance. 7. Military Combat. 8. Nine Changes. 9. Maneuvering the Army. 10. Configurations of Terrain. 11. Nine Terrains. 12. Incendiary Attacks. 13. Employing Spies -- Tomb Texts and Lost Writings -- Notes to the General Introduction and Historical Background -- Selected Notes to the Introduction -- Notes to the Translation -- Notes to the Tomb Texts and Lost Writings.",
   keywords = "intentionality, philosophy, honours reading",
   year = "1994",
   
   }
   




3. Gerard Tel. Introduction to distributed algorithms. Cambridge University Press, Cambridge ; New York, 1994.
   Note: 94016147 Gerard Tel. Includes bibliographical references (p. 512-524) and index. 1. Introduction: Distributed Systems -- Pt. 1. Protocols. 2. The Model. 3. Communication Protocols. 4. Routing Algorithms. 5. Deadlock-free Packet Switching -- Pt. 2. Fundamental Algorithms. 6. Wave and Traversal Algorithms. 7. Election Algorithms. 8. Termination Detection. 9. Anonymous Networks. 10. Snapshots. 11. Synchrony in Networks -- Pt. 3. Fault Tolerance. 12. Fault Tolerance in Distributed Systems. 13. Fault Tolerance in Asynchronous Systems. 14. Fault Tolerance in Synchronous Systems. 15. Stabilization -- Pt. 4. Appendices. A. Pseudocode Conventions. B. Graphs and Networks.
   Keywords: agents, algorithms, honours reading, coordination.
   

   @Book{Tel1994,
   author = "Gerard Tel",
   title = "Introduction to distributed algorithms",
   publisher = "Cambridge University Press",
   address = "Cambridge ; New York",
   note = "94016147 Gerard Tel. Includes bibliographical references (p. 512-524) and index. 1. Introduction: Distributed Systems -- Pt. 1. Protocols. 2. The Model. 3. Communication Protocols. 4. Routing Algorithms. 5. Deadlock-free Packet Switching -- Pt. 2. Fundamental Algorithms. 6. Wave and Traversal Algorithms. 7. Election Algorithms. 8. Termination Detection. 9. Anonymous Networks. 10. Snapshots. 11. Synchrony in Networks -- Pt. 3. Fault Tolerance. 12. Fault Tolerance in Distributed Systems. 13. Fault Tolerance in Asynchronous Systems. 14. Fault Tolerance in Synchronous Systems. 15. Stabilization -- Pt. 4. Appendices. A. Pseudocode Conventions. B. Graphs and Networks.",
   keywords = "agents, algorithms, honours reading, coordination",
   year = "1994",
   
   }
   

1. Chitta Baral and Michael Gelfond. Logic Programming and Knowledge Representation. Journal of Logic Programming, 19/20:73--148, 1994.
   Keywords: logic programming, honours reading.
   

   @Article{Baral1994,
   author = "Chitta Baral and Michael Gelfond",
   title = "Logic Programming and Knowledge Representation",
   journal = "Journal of Logic Programming",
   volume = "19/20",
   pages = "73--148",
   keywords = "logic programming, honours reading",
   year = "1994",
   
   }
   




2. Walter F. Bischof and Terry Caelli. Learning Structural Descriptions of Patterns: A New Technique for Conditional Clustering and Rule Generation. Pattern Recognition, 27(5):689--97, 1994.
   Keywords: machine learning, inductive logic programming, honours reading.
   

   @Article{Bischof1994,
   author = "Walter F. Bischof and Terry Caelli",
   title = "Learning Structural Descriptions of Patterns: {A} New Technique for Conditional Clustering and Rule Generation",
   journal = "Pattern Recognition",
   volume = "27",
   number = "5",
   pages = "689--97",
   keywords = "machine learning, inductive logic programming, honours reading",
   year = "1994",
   
   }
   




3. Tom Bylander. The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence, 69(1-2):165--204, 1994.
   Keywords: planning, honours reading, computational complexity.
   

   @Article{Bylander1994,
   author = "Tom Bylander",
   title = "The Computational Complexity of Propositional {STRIPS} Planning",
   journal = "Artificial Intelligence",
   volume = "69",
   number = "1-2",
   pages = "165--204",
   keywords = "planning, honours reading, computational complexity",
   year = "1994",
   
   }
   




4. Yves Deville and Kung-Kiu Lau. Logic Program Synthesis. Journal of Logic Programming, 19/20:321--350, 1994.
   Keywords: logic programming, inductive logic programming, honours reading.
   

   @Article{Deville1994,
   author = "Yves Deville and Kung-Kiu Lau",
   title = "Logic Program Synthesis",
   journal = "Journal of Logic Programming",
   volume = "19/20",
   pages = "321--350",
   keywords = "logic programming, inductive logic programming, honours reading",
   year = "1994",
   
   }
   




5. Jörg-Uwe Kietz and Saso Dzeroski. Inductive Logic Programming and Learnability. SIGART Bulletin, 5(1):22--32, 1994.
   Keywords: inductive learning, inductive logic programming, honours reading.
   

   @Article{Kietz1994,
   author = "J{\"{o}}rg-Uwe Kietz and Saso Dzeroski",
   title = "Inductive Logic Programming and Learnability",
   journal = "SIGART Bulletin",
   volume = "5",
   number = "1",
   pages = "22--32",
   keywords = "inductive learning, inductive logic programming, honours reading",
   year = "1994",
   
   }
   




6. Raymond J. Mooney and John M. Zelle. Integrating ILP and EBL. SIGART Bulletin, 5(1):12--21, 1994.
   Keywords: inductive logic programming, machine learning, honours reading.
   

   @Article{Mooney1994,
   author = "Raymond J. Mooney and John M. Zelle",
   title = "Integrating {ILP} and {EBL}",
   journal = "SIGART Bulletin",
   volume = "5",
   number = "1",
   pages = "12--21",
   keywords = "inductive logic programming, machine learning, honours reading",
   year = "1994",
   
   }
   




7. David Morley, Michael P. Georgeff, and Anand S. Rao. A Monotonic Formalism for Events and Systems of Events. Journal of Logic and Computation, 4(5):701--720, 1994.
   Keywords: agentlab, model theory, procedural reasoning.
   

   @Article{Morley1994,
   author = "David Morley and Michael P. Georgeff and Anand S. Rao",
   title = "A Monotonic Formalism for Events and Systems of Events",
   journal = "Journal of Logic and Computation",
   volume = "4",
   number = "5",
   pages = "701--720",
   keywords = "agentlab, model theory, procedural reasoning",
   year = "1994",
   
   }
   




8. Stephen Muggleton and Luc De Raedt. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming, 19/20:629--679, 1994.
   Keywords: inductive logic programming, honours reading.
   

   @Article{Muggleton1994,
   author = "Stephen Muggleton and Luc De Raedt",
   title = "Inductive Logic Programming: Theory and Methods",
   journal = "Journal of Logic Programming",
   volume = "19/20",
   pages = "629--679",
   keywords = "inductive logic programming, honours reading",
   year = "1994",
   
   }
   




9. A. R. Pearce, T. Caelli, and W. F. Bischof. Rulegraphs for graph matching in pattern recognition. Pattern Recognition, 27(9):1231--47, 1994.
   Keywords: graph theory, machine learning, agentlab.
   
   

   Abstract:

   "In pattern recognition, the graph matching problem involves the matching of a sample data graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present rulegraphs, a new method that combines the graph matching approach with rule-based approaches from machine learning. This new method reduces the cardinality of the (NP-complete) graph matching problem by replacing model part, and their relational attribute states, by rules which depict attribute bounds and evidence for different classes. We show how rulegraphs, when combined with techniques for checking feature label-compatibilities, not only reduce the search space but also improve the uniqueness of the matching process. (37 References)."

   [download paper ]
   

   @Article{Pearce1994b,
   author = "A. R. Pearce and T. Caelli and W. F. Bischof",
   title = "Rulegraphs for graph matching in pattern recognition",
   journal = "Pattern Recognition",
   volume = "27",
   number = "9",
   pages = "1231--47",
   abstract = "In pattern recognition, the graph matching problem involves the matching of a sample data graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present rulegraphs, a new method that combines the graph matching approach with rule-based approaches from machine learning. This new method reduces the cardinality of the (NP-complete) graph matching problem by replacing model part, and their relational attribute states, by rules which depict attribute bounds and evidence for different classes. We show how rulegraphs, when combined with techniques for checking feature label-compatibilities, not only reduce the search space but also improve the uniqueness of the matching process. (37 References).",
   URL = "http://www.agentlab.unimelb.edu.au/papers/pearce1994b.pdf",
   keywords = "graph theory, machine learning, agentlab",
   year = "1994",
   
   }
   




10. A. R. Pearce, T. Caelli, and W. F. Bischof. Learning relational structures: applications in computer vision. Applied Intelligence, 4(3):257--68, 1994.
    Note: Netherlands.
    Keywords: graph theory, machine learning, agentlab.
    
    

    Abstract:

    "We present and compare two new techniques for learning relational structures (RSs) as they occur in 2D pattern and 3D object recognition. These techniques, namely, evidence-based networks (EBS-NNets) and Rulegraphs combine techniques from computer vision with those from machine learning and graph matching. The EBS-NNet has the ability to generalize pattern rules from training instances in terms of bounds on both unary (single part) and binary (part relation) numerical features. It also learns the compatibilities between unary and binary feature states in defining different pattern classes. Rulegraphs check this compatibility between unary and binary rules by combining evidence theory with graph theory. The two systems are tested and compared using a number of different pattern and object recognition problems. (21 References)."

    [download paper ]
    

    @Article{Pearce1994,
    author = "A. R. Pearce and T. Caelli and W. F. Bischof",
    title = "Learning relational structures: applications in computer vision",
    journal = "Applied Intelligence",
    volume = "4",
    number = "3",
    pages = "257--68",
    note = "Netherlands.",
    abstract = "We present and compare two new techniques for learning relational structures (RSs) as they occur in 2D pattern and 3D object recognition. These techniques, namely, evidence-based networks (EBS-NNets) and Rulegraphs combine techniques from computer vision with those from machine learning and graph matching. The EBS-NNet has the ability to generalize pattern rules from training instances in terms of bounds on both unary (single part) and binary (part relation) numerical features. It also learns the compatibilities between unary and binary feature states in defining different pattern classes. Rulegraphs check this compatibility between unary and binary rules by combining evidence theory with graph theory. The two systems are tested and compared using a number of different pattern and object recognition problems. (21 References).",
    URL = "http://www.agentlab.unimelb.edu.au/papers/pearce1994.pdf",
    keywords = "graph theory, machine learning, agentlab",
    year = "1994",
    
    }
    




11. L. Sonenberg and R. Topor. A preferred model semantics for inheritance networks. Methods of Logic in Computer Science, 1(1):3--18, 1994.
    Note: USA.
    Keywords: agentlab.
    
    

    Abstract:

    "We describe a representation of inheritance networks with strict and defeasible links as first-order theories in which different priority policies can be expressed within the theory. We present a {"}preferred model{"} semantics for such theories by adapting the perfect model semantics for logic programs, and briefly discuss relationships between this semantics and other proposed semantics for inheritance networks. (26 References)."

    
    

    @Article{Sonenberg1994,
    author = "L. Sonenberg and R. Topor",
    title = "A preferred model semantics for inheritance networks",
    journal = "Methods of Logic in Computer Science",
    volume = "1",
    number = "1",
    pages = "3--18",
    note = "USA.",
    abstract = "We describe a representation of inheritance networks with strict and defeasible links as first-order theories in which different priority policies can be expressed within the theory. We present a {"}preferred model{"} semantics for such theories by adapting the perfect model semantics for logic programs, and briefly discuss relationships between this semantics and other proposed semantics for inheritance networks. (26 References).",
    keywords = "agentlab",
    year = "1994",
    
    }
    




12. Bradley S. Stewart, Ching-Fang Liaw, and Chelsea C. White. A bibliography of heuristic search research through 1992. IEEE Transactions on Systems, Man, and Cybernetics, 24(2):268--293, 1994.
    Keywords: algorithms, honours reading.
    

    @Article{Stewart1994,
    author = "Bradley S. Stewart and Ching-Fang Liaw and Chelsea C. White",
    title = "A bibliography of heuristic search research through 1992",
    journal = "IEEE Transactions on Systems, Man, and Cybernetics",
    volume = "24",
    number = "2",
    pages = "268--293",
    keywords = "algorithms, honours reading",
    year = "1994",
    
    }
    

1. J. Y. Halpern, Y. Moses, and M. Y. Vardi. Algorithmic knowledge. In Theoretical Aspects of Reasoning About Knowledge. Proceedings of the Fifth Conference (TARK 1994)., pages 255--66, 1994. Morgan Kaufmann Publishers.
   Note: Fagin R San Francisco, CA, USA. Proceedings of Fifth Conference on Theoretical Aspects of Reasoning about Knowledge (TARK V). Pacific Grove, CA, USA. 13-16 March 1994.
   Keywords: agents, honours reading, epistemic logic, model theory.
   
   

   Abstract:

   "The standard model of knowledge in multi-agent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this does not turn out to be a problem. Knowledge is viewed as being ascribed by the system designer to the agents; agents are not assumed to compute their knowledge in any way, nor is it assumed that they can necessarily answer questions based on their knowledge. Nevertheless, in many applications that we are interested in, agents need to act on their knowledge. In such applications, an externally ascribed notion of knowledge is insufficient: clearly an agent can base his actions only on what he explicitly knows. Furthermore, an agent that has to act on his knowledge has to be able to compute this knowledge; we do need to take into account the algorithms available to the agent, as well as the {"}effort{"} required to compute knowledge. We show how the standard model can be modified in a natural way to take the computational aspects of knowledge into account. (30 References)."

   
   

   @InProceedings{Halpern1994,
   author = "J. Y. Halpern and Y. Moses and M. Y. Vardi",
   title = "Algorithmic knowledge",
   booktitle = "Theoretical Aspects of Reasoning About Knowledge. Proceedings of the Fifth Conference (TARK 1994).",
   publisher = "Morgan Kaufmann Publishers",
   pages = "255--66",
   note = "Fagin R San Francisco, CA, USA. Proceedings of Fifth Conference on Theoretical Aspects of Reasoning about Knowledge (TARK V). Pacific Grove, CA, USA. 13-16 March 1994.",
   abstract = "The standard model of knowledge in multi-agent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this does not turn out to be a problem. Knowledge is viewed as being ascribed by the system designer to the agents; agents are not assumed to compute their knowledge in any way, nor is it assumed that they can necessarily answer questions based on their knowledge. Nevertheless, in many applications that we are interested in, agents need to act on their knowledge. In such applications, an externally ascribed notion of knowledge is insufficient: clearly an agent can base his actions only on what he explicitly knows. Furthermore, an agent that has to act on his knowledge has to be able to compute this knowledge; we do need to take into account the algorithms available to the agent, as well as the {"}effort{"} required to compute knowledge. We show how the standard model can be modified in a natural way to take the computational aspects of knowledge into account. (30 References).",
   keywords = "agents, honours reading, epistemic logic, model theory",
   year = "1994",
   
   }
   




2. J. R. Josephson. Inductive Generalizations are Abductions. In Position paper for the Workshop on Abductive and Inductive Reasoning, 1994.
   Keywords: abductive logic programming, inductive logic programming, philosophy, honours reading.
   

   @InProceedings{Josephson1994,
   author = "J. R. Josephson",
   title = "Inductive Generalizations are Abductions",
   booktitle = "Position paper for the Workshop on Abductive and Inductive Reasoning",
   keywords = "abductive logic programming, inductive logic programming, philosophy, honours reading",
   year = "1994",
   
   }
   




3. David Kinny, Ljungberg, Anand Rao, Liz Sonenberg, Gil Tidhar, and Werner. Planned Team Activity. In MAAMAW 1992: Artificial Social Systems, LNCS 830, pages 227--256, 1994.
   Keywords: agentlab, teamwork.
   

   @InProceedings{Kinny1994,
   author = "David Kinny and Ljungberg and Anand Rao and Liz Sonenberg and Gil Tidhar and Werner",
   title = "Planned Team Activity",
   booktitle = "MAAMAW 1992: Artificial Social Systems, LNCS 830",
   pages = "227--256",
   keywords = "agentlab, teamwork",
   year = "1994",
   
   }
   




4. Mehmet A. Orgun and Wanli Ma. An Overview of Temporal and Modal Logic Programming. In First International Conference on Temporal Logic; appears in Lecture Notes in omputer Science; Vol. 827, pages 445--479, 1994. Springer-Verlag London, UK.
   Keywords: modal logic, honours reading.
   

   @InProceedings{Orgun1994,
   author = "Mehmet A. Orgun and Wanli Ma",
   title = "An Overview of Temporal and Modal Logic Programming",
   booktitle = "First International Conference on Temporal Logic; appears in Lecture Notes in omputer Science; Vol. 827",
   publisher = "Springer-Verlag London, UK",
   pages = "445--479",
   keywords = "modal logic, honours reading",
   year = "1994",
   
   }
   

1. T. Gabric, Howden, Emma Norling, Gil Tidhar, and Liz Sonenberg. Multi-agent Design of a Traffic Flow Control System. Technical Report 94/24, Department of Computer Science, University of Melbourne, Australia, 1994.
   Keywords: agentlab.
   

   @TechReport{Gabric1994,
   author = "T. Gabric and Howden and Emma Norling and Gil Tidhar and Liz Sonenberg",
   title = "Multi-agent Design of a Traffic Flow Control System",
   institution = "Department of Computer Science, University of Melbourne, Australia",
   number = "94/24",
   type = "Technical Report",
   keywords = "agentlab",
   year = "1994",
   
   }
   

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