|Time||Delegate||Institution||Title and abstract||Draft paper|
|0910-1010, 1 Dec||Setsuo Arikawa||Kyushu University||Click here|
|1010-1110, 1 Dec||Marcel Turcotte||Imperial Cancer Research Fund||Click here|
|1400-1500, 1 Dec||Derek Sleeman||University of Aberdeen||Click here|
|1500-1600, 1 Dec||David Page||University of Louisville||Click here|
|1630-1730, 1 Dec||Akihiro Yamamoto||Hokkaido University||Click here|
|1730-1830, 1 Dec||Ted Briscoe||University of Cambridge||Click here|
|0900-1000, 2 Dec||Luc Steels||Vrije Universiteit Brussel||Click here|
|1000-1100, 2 Dec||William Cohen||AT&T||Click here|
|1130-1230, 2 Dec||James Cussens||University of York||Click here|
|1400-1500, 2 Dec||Mike Georgeff||Australian AI Institute||Click here|
|1500-1600, 2 Dec||Alan Frisch||University of York||Click here|
|1630-1730, 2 Dec||Andrei Voronkov||Univerity of Uppsala||Click here|
|1730-1830, 2 Dec||Luc De Raedt||Katholieke Universiteit Leuven||Click here|
|0900-1000, 3 Dec||Kurt Konolige||SRI International||Click here|
|1000-1100, 3 Dec||Javier Lerch||Carnegie Mellon University||Click here|
|1130-1230, 3 Dec||Stephen Muggleton||University of York||Click here|
|1400-1500, 3 Dec||Claude Sammut||University of New South Wales||Click here|
|1500-1600, 3 Dec||Dorian Suc||University of Ljubljana||Click here|
|1630-1800, 3 Dec||Wayne Wobcke||BT Labs||Click here|
De Raedt, Luc
link to individual abstract click on delegate name/to link to home page
click on speaker's name
Title: Discovery Science: A new everlasting science
With the drastic advancement of computer technology, a huge amount of data is regularly compiled in computers. These include data from experiments, observations, business activities, etc., and the scale of measure becomes over several hundreds Giga bytes. An emergent requirement from the current social and scientific circumstances is to develop efficient methods with computers which enable automatic discoveries of scientific knowledge and decision making rules. In order to meet such a social requirement from the front, we have just started a three year project: Grant-in-Aid for Scientific Research on Priority Area "Discovery Science" sponsored by Ministry of ESSC, Japan. This project intends to develop new methods for knowledge discovery, install, network environments for knowledge discovery, and establish the Discovery Science as a new area of Computer Science. A systematic research is planned that ranges over philosophy, logic, reasoning, computational learning and system developments. This lecture describes the outline of the project including how it has been prepared.
Title: The Acquisition of Grammar in an Evolving Population of Language Agents
Human language acquisition, and in particular the acquisition of grammar, is a partially-canalized, strongly-biased but robust and efficient procedure. For example, children prefer to induce compositional rules (e.g. Wanner and Gleitman, 1982) despite peripheral use of non-compositional constructions, such as idioms, in every attested human language. And, most parameters of grammatical variation set during language acquisition appear to have default values retained in the absence of robust counter-evidence (e.g.Bickerton, 1984; Lightfoot, 1989). A variety of explanations have been offered for the emergence of a partially-innate language acquisition device (LAD) with such properties, such as exaption of a spandrel (Gould, 1987), biological saltation (Chomsky, 1972) or genetic assimilation (Pinker and Bloom, 1990). But none provide a coherent account of both the emergence and maintenance of a LAD in an evolving population.
The account offered here is that an embryonic LAD emerged via exaption
of general-purpose (Bayesian) learning mechanisms (e.g. Staddon, 1983)
to a specifically-linguistic mental representation capable of expressing
mappings from the `language of thought' to `realizable' encodings of propositions
expressed in the language of thought. However, the selective pressure favouring
such an exaption, and its subsequent maintenance and refinement, is only
coherent given a coevolutionary scenario in which a (proto)language supporting
successful communication within a population had already itself evolved
on a historical timescale (e.g. Hurford, 1987; Kirby, 1998; Steels, 1997)
and continued to coevolve with the LAD (e.g. Briscoe, 1997, in press).
This account is supported by the results of a number of computational simulations
of evolving populations of software agents acquiring and communicating
with coevolving structured languages.
The model behind the simulations suggests a new dynamic framework forthe study of communication systems in general, and human language in particular, which both incorporates the insights gained from formalizing a language as static well-formed stringset (Chomsky, 1957) and extends them by embedding this model in an evolving population of distributed language agents. The practical implication of this framework for natural language processing is that development of static hand-coded systems should be replaced by development of autonomous software agents capable of adapting to their linguistic environment.
Title: Whirl: A Word-based Information Representation Language
We describe WHIRL, an ``information representation language'' that synergistically combines properties of logic-based and text-based representation systems. WHIRL is a subset of non-recursive Datalog that has been extended by introducing an atomic type for textual entities, an atomic operation for computing textual similarity, and a ``soft'' semantics---that is, inferences in WHIRL are associated with numeric scores, and presented to the user in decreasing order by score. We show that WHIRL strictly generalizes both IR ranked retrieval, and logical deduction; that non-trivial queries concerning large databases can be answered efficiently; that WHIRL can be used to accurately integrate data from heterogeneous information sources, such as those found on the Web; that WHIRL can be used effectively for inductive classification of text; and finally, that WHIRL can be used to generate extraction programs for structured documents semi-automatically.
Title: Robots as Physical Agents
Indoor mobile robots are becoming reliable enough in navigation tasks to consider working with teams of robots. Software agents are also becoming increasingly popular as a means of fashioning complex systems that integrate a variety of human interface, database, and reasoning components. In this paper we develop a view of multi-robot systems in which the robots are integrated as physical agents in a larger system that also includes software agents. We argue that this approach allows for timely development and deployment of complex systems that integrate physical, communicative, and inferential capabilities. To illustrate the general ideas, we have implemented a multiple-robot testbed using SRI's Open Agent Architecture (OAA) and Saphira robot control system. This testbed addresses issues of robot communication, multiple-agent planning, and human-robot interaction. In its current implementation, the testbed ties together physical robots and a set of software agents on the Internet to plan and act in coordination users communicate with the robots using a variety of multimodal inputs: pen, voice, and keyboard. The robust capabilities of the OAA and Saphira enabled us to design and implement a winning team of three robots for the AAAI robotics contest (Summer 1996) in the six weeks.
Title: The Role of Representation in Behavioural Cloning
Behavioural Cloning seeks to build explanations of human skill by inducing rules of behaviour from performance traces. Finding appropriate representations of the state of the world is crucial to building models that are accurate and transparent. This paper discusses the role that relational representations play in Behavioural Cloning. The domain of application in our case is learning to pilot a simulated aircraft. The simulator used is capable of generating symbolic descriptions of the visual scene viewed by the pilot. We discuss the advantages and disadvantages of learning in such a rich domain and also how high-level features can be constructed using relational learning.
Title: A knowledgeable system for knowledge discovery.
Traditionally, knowledge discovery systems tend to be domain-specific; on the other hand, Data-mining systems tend to be domain-independent and very much data-driven. In the latter case, usually, the only domain-tuning allowed is in the choice of the language used to describe the data and the hypotheses. This leads to difficulties when applying the data-mining methodology to tasks in complex domains, such as in medicine, as the number of hypotheses are often too numerous to be analysed fully; some of them contradict basic domain knowledge; many are useless because, despite being correct, they are not novel. We describe a system for knowledge discovery which is both domain independent and able to acquire through a collaborative process some high-level domain knowledge. The system has two modules. The first is a standard data-mining algorithm which generates associations. The second allows the user to label some of the associations as either consistent (wrt a domain theory), inconsistent (wrt a theory) or innovative. Once acquired, the system then generalizes these patterns. Subsequently the system uses the generalized patterns to classify the remaining associations & it simply presents only those which it believes are innovative to the Domain expert.
We call this second module, a knowledge-based filter & we believe it can be used in a wide variety of domains. (Clearly the generalization process will be more effective if some domain-specific background knowledge is available).
Speaker: Dorian Suc,
Title: Symbolic and qualitative skill reconstruction,
Controlling a complex dynamic system, such as a plane or a crane, usually requires a skilled operator. Such a control skill is typically hard to reconstruct through introspection. Therefore an attractive approach to the reconstruction of control skill involves machine learning from operators' control traces, also known as behavioural cloning.
In the most common approach to behavioural cloning, a controller is induced as a direct mapping from system states to actions. Unfortunately, such controllers usually lack typical elements of human control strategies, such as subgoals or desired trajectory and do not replicate the robustness of the human control skill. In this paper we apply the GoldHorn program to induce from the control traces a set of symbolic constraints. Those constraints describe the operator's trajectory and are then used together with locally weighted regression model to determine the next action.
Using the crane problem in a case study, this approach showed significant improvements both in terms of control performance and transparency of induced clones. Moreover, generalizing the trajectory into qualitative strategy shows the potentials of such an approach.
Title: Application of Inductive Logic Programming to Discover Rules Explaining Protein Three-Dimensional Structure}
For the last three decades, understanding protein structure has been and still is a challenging problem for molecular biology. The problem is to identify rules which relate the local structure to the complex three-dimensional fold. There are now classification schemes for some 8000 three-dimensional folds. To gain further insights into protein structure, Inductive Logic Programming (ILP) has been applied to derive new principles governing the formation of protein folds, such as common substructures and the relationship between local sequence and tertiary structure.
Title : Hypothesis Construction and Beyond it.
Hypothesis construction is a basic activity of both abductive and inductive systems. It is to find hypotheses H from a positive example E with the support of a given background theory B when E cannot proved from B. Some hypothesis construction procedures find H by fixing. incomplete proofs of E from B. In this talk we compare the potential of several hypothesis construction procedures of such type. Moreover, we discuss how we should justify the generated hypotheses H by using network agents. We also show the relation between abductive inference and inductive inference from the viewpoint of fixing incomplete proofs.
Title: Formalizing bottom-up and top-down proof search in one logical calculus.
Automatic reasoning methods based on proof-search in sequent calculi are divided in two groups of methods. The tableau-based methods implement the top-down proof-search, while the inverse method implements the bottom-up proof-search. We show how to design a single calculus for the bottom-up and top-down proof-search for a number of logics.
Title: Solving Constraint Satisfaction Problems with MV-Resolution
Though resolution and constraint satisfaction problems are two of the most developed areas in artificial intelligence, almost no connections between the two have been made. This talk explores the relationship between resolution and constraint satisfaction problems. The exploration results in the development of the MV-resolution rule of inference, a generalisation of ordinary resolution that can be used to solve constraint satisfaction problems. This talk proves that a clausal-form constraint satisfaction problem is backtrack-free if its constraints are closed under MV-resolution. This generalises the usual completeness result for resolution by telling us not only what happens when an unsatisfiable set of clauses is closed under resolution but also what happens when a satisfiable set of clauses is closed under resolution.
Title: How Language Bootstraps Cognition
Where do perceptually grounded categories come from? Some researchers claim that they are innate, explaining the rapid origins of such categories in children even with a poor stimulus. Others claim they are learned, explaining that categories are adapted to the environments and tasks humans encounter. This talk presents a third `ecological' approach to category formation.
I propose a system by which discrimination networks, capable to perform
categorial distinctions, spontaneously grow, relatively independently of
specific examples. The networks are pruned to eliminate distinctions that
were not relevant in the environment. The continuous growth and pruning
dynamics leads to constant adaptation and to anticipation of distinctions
even if no examples were
seen yet. I will show through software simulations and experiments with physical robotic agents that the ecological approach leads to an adequate categorial repertoire. Next I will show that linguistic interaction can be a driving force to sparkle a spiraling increase in the ontological complexity of an agent. The ontology formation mechanism can be coupled to adaptive language games through which a shared lexicon spontaneously self-organises itself. Again, examples from software simulations and experiments with robotic
agents are presented to demonstrate that this approach is effective.
The main conclusion is that a complex adaptive systems approach to the
origins of ontologies and lexicons is possible and presents a viable alternative,
both to a nativist account and to an inductive, connectionist account.
Title: Modeling Time Pressure and Individual Differences in a Real-Time Dynamic Decision Making Task
This research investigates the impact of time pressure and individual differences on learning in a Real-Time Dynamic Decision Making (RTDDM) task. Our empirical results indicate that high time pressure generates high cognitive loads inhibiting learning. The results also show that high time pressure have a differential impact on the learning of individuals with high or low Working Memory (WM) and rule inference capacity. We are in the process of building a cognitive model based on ACT-R that explains these two phenomena. Our cognitive model simulates learning by recognizing regularities in the decision task, and building "chunks" that guide decision
making. This cognitive model explains the impact of time pressure and WM capacity on learning by varying the number of chunks acquired by the system given alternative time pressure conditions and individual differences.
Speaker: Wayne Wobcke
Title: Machine Intelligence Research at BT
In this talk, we examine the issues for Machine Learning posed by the problem of developing human-centred intelligent systems. Thesystems we have in mind are software systems that must continually adapt their performance to the changing requirements and preferences of the user. Thus in this respect, human-centred intelligent systems are similar to robotic systems that must adapt to a continuously changing environment. We describe the Intelligent Personal Assistant (IPA), an agent-based application recently developed at BT Laboratories, that provides assistance with time, information and communication management. We show how some of the problems of desiging adaptive software assistants have been solved within the context of the IPA, and discuss the particular
types of learning that will need to be incorporated into the next version of the system.
Speaker: Wayne Wobcke (Kevin Irwig)
Title: Multi-Agent Reinforcement Learning with Vicarious Rewards
Reinforcement learning is the problem faced by an agent that must learn behaviour through trial-and-error interactions with a dynamic
environment. In a multi-agent setting, the problem is often further complicated by the need to take into account the behaviour of other
agents in order to learn to perform effectively. Issues of coordination and cooperation must be addressed; in general, it is not sufficient for each agent to act selfishly in order to arrive at a globally optimal strategy. In this work, we apply the Adaptive
Heuristic Critic (AHC) and Q-learning algorithms to agents in a simple artificial multi-agent domain based on the Tileworld.
We experimentally compare the performance of the AHC and Q-learning algorithms to each other as well as to a hand-coded greedy strategy. The overall result is that AHC agents perform better than the others, particularly when many other agents are present or the world is dynamic. We also examine the notion of global optimality in this system, and present a simple method of encouraging agents to learn cooperative behaviour, which we call vicarious reinforcement. The main result of this work is that agents that receive additional vicarious reinforcement perform better than selfish agents, even though the task being performed here is not inherently cooperative.
As we all know, but seem not to have fully understood (at least in the
way physicists have) the world is complex and dynamic, a place where chaos
is the norm, not the exception. We also know that computational systems
have practical limitations, which limit the information they can access
and the computations they can perform. Conventional software systems
are designed for static worlds with perfect knowledge---we are instead
interested in environments that are dynamic and uncertain (or chaotic),
and where the computational system only has a local view of the world (i.e.,
has limited access to information) and is resource bounded (i.e., has finite
computational resources). These constraints have certain fundamental implications
for the design of the underlying computational architecture. In this talk,
I will attempt to show that Beliefs, Desires, Intentions, and Plans are
an essential part of the state of such systems.
Title: Relational Reinforcement Learning for Intelligent Agents
Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the effects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with
reinforcement learning. In particular, relational reinforcement learning allows us to employ structural representations, make abstraction of specific goals pursued and exploit the results of previous learning phases when addressing new (more complex) situations. First results on relational reinforcement learning have already been published elsewhere (cf. references below).
In this presentation, we shall however analyse relational reinforcement learning from the view point of intelligent agents. To this aim we shall discuss further experiments (in a simple office world) and argue that relational reinforcement provides new possibilities for learning agents.
zeroski, S., De Raedt, L. and Blockeel, H. Relational Reinforcement Learning, in Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann, 1998.
zeroski, S., De Raedt, L. and Blockeel, H. Relational Reinforcement Learning, in Proceedings of the 8th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Springer Verlag, 1998.