Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

How to Do Things With Words

An Introduction to MultiAgent Systems.

1.. HARD CASES 5. Legal Rights A. Legislation . . . We might therefore do well to consider how a philosophical judge might develop, in appropriate cases, theories of what legislative purpose and legal principles require. We shall find that he would construct these theories in the same manner as a philosophical referee would construct the character of a game. I have invented, for this purpose, a lawyer of superhuman skill, learning, patience and acumen, whom I shall call Hercules. I suppose that Hercules is a judge in some representative American jurisdiction. I assume that he accepts the main uncontroversial constitutive and regulative rules of the law in his jurisdiction. He accepts, that is, that statutes have the general power to create and extinguish legal rights, and that judges have the general duty to follow earlier decisions of their court or higher courts whose rationale, as l

Taking Rights Seriously

In this paper we introduce the so-called Beliefs-Obligations-Intentions-Desires or BOID architecture. It contains feedback loops to consider all effects of actions before committing to them, and mechanisms to resolve conflicts between the outputs of its four components. Agent types such as realistic or social agents correspond to specific types of conflict resolution embedded in the BOID archecture.

The BOID architecture: conflicts between beliefs, obligations, intentions and desires

In this paper we consider goal generation in cognitive agent architectures. We show how goal generation can be described in terms of interaction between mental attitudes biased by agent types such as realistic, social, selfish and stable. We introduce a generic BOID architecture and agent type specific BOID architectures, in which goals are generated from conditional beliefs, obligations, intentions and desires. We implement the BOID architectures by relating conditional mental attitudes to components, goal generation to extension construction, and agent types to constraints on priority functions.

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Goal generation in the BOID architecture

set of n-tuples description Vw(λ~x.φ) {w | Vw(λ~x.φ) = E} (E ⊆ Dn) question proposition partition answerhood {v | Vv(λ~x.φ) = Vw(λ~x.φ)} {{v | Vv(λ~x.φ) = Vw(λ~x.φ)} | w ∈ W} conditions Figure 3.4: Extension and intension of assertions, abstracts and questions (33) Does John love Mary? ?love(john,mary) extension at w: {v | Vv(love(john,mary)) = Vw(love(john,mary))} intension set: {{v | Vv(love(john,mary)) = Vw(love(john,mary))} | w ∈ W} The question relation of the question in example (34) is λx.love(john, x). The extension at w is the complete answer at w: the set of worlds that agree with w on the objects that satisfy the question relation. Each answer roughly corresponds to a subset E of the set of objects that are initially plausible answer candidates. The intension can again be derived by abstracting over w. Worlds that are indistinguishable with respect to their answers, are grouped together. Therefore a wh-question produces a partition in as many blocks, as there are subsets of the set of plausible answer candidates. (34) Whom does John love? ?x.love(john.x) extension at w: {v | Vv(λx.love(john, x)) = Vw(λx.love(john, x)) ) intension set: {{v | Vv(λx.love(john, x)) = Vw(λx.love(john, x))} | w ∈ W} Now we can motivate definition 8 and 9 of extension and intension for each type of expression in the language. The intuitions are summarised in figure 3.4. These notions can be expressed more elegantly in a two-sorted type-theory (Groenendijk and Stokhof 1989). For an assertion, the extension is a truth value that indicates whether the asserted is satisfied at the world of evaluation. The intension is a characteristic function: a function from worlds to truth values. In general, the intension of an expression is a function that maps worlds onto the extensions. Based on the intension, we can derive what one may call the intension set: the set of worlds that share a particular extension value. What value that is, depends on the success conditions of the particular way the expression is used. For an assertion, that value is typically the truth value ‘1’. For a term, the extension is its interpretation in a world: an object from the domain, as in definition 6. Its intension is a function from worlds to objects: an individual concept. We can again derive an intension set. A minimal requirement is that the term is defined in a particular world. So a natural intension set for a term would be the set of worlds for which the extension is defined. This corresponds to the presupposition of the term. An n-place abstract expresses some (question) relation. Its extension is analogous to the interpretation of a basic predicate symbol. It denotes the set of n-tuples of objects that satisfy the relation at a particular world. Again, we can define an intension set. Any set of applicable tuples E ⊆ Dn may count as the typical extension value of an n-place abstract. The set of worlds with non-empty answer sets corresponds to the conventional presupposition of the question corresponding to the n-place abstract. 3.3. UPDATE SEMANTICS WITH QUESTIONS 83 Finally, a question has as its extension in a world, the complete answer to the question at that world. Answers are propositions, modelled by a set of worlds. In this case, the set of worlds that agree on their extension of the n-place abstract. So the extension of a question equals the intension set of an abstract. The intension of an question is again the functin that maps worlds onto their extensions. The corresponding intension set is therefore a partition of the set of worlds where the question makes sense. Here is the definition of the extension of an expression relative to a particular context of evaluation: a model, a world and an assignment. Sequences of expressions φ;ψ are dealt with when we come to updates, in definition 15. Definition 8 (Extension) Given a model M, define for each φ ∈ L0 the extension at w under g Vw,g(φ) = { 1, if M,w, g |= φ 0, otherwise Vw,g(?~x.φ) = {v | Vv,g(λ~x.φ) = Vw,g(λ~x.φ)} where Vw,g(λ~x.φ) = {~ d ∈ Dn | M, g[~x 7→ ~ d],w |= φ} 2 Note that the definition for assertions is equal to that of abstracts for ~x = 〈〉 when you take {〈〉} as ‘1’ and ∅ as ‘0’. Also yes/no questions are an instance of the general case for questions. If the empty sequence 〈〉 makes the question relation true, this may be classified as a ‘yes’. If no sequence makes the question relation true, the answer was ‘no’. Proposition 1 (Yes/No Questions) For each model M, world w and assignment g Vw,g(?φ) = { {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = {〈〉}}, if M, g,w |= φ (’yes’) {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = ∅}, otherwise (’no’) Proof By definition. 2 The intension of an expression is the extension abstracted over the context. Given an assignment g, this produces for each φ a representative function 〈[φ]〉g from worlds to extensions. Definition 9 (Intension) Given model M and assignment g define for each φ ∈ L 〈[φ]〉g = λv.Vv,g(φ). 2 Based on the representative function we can derive an equivalence relation. Worlds are grouped according to their extensions. Using the earlier notation for equivalence under a given function we write '〈[φ]〉g . The notion of [[φ]]g, the set of worlds that satisfies a certain expression φ under g corresponds to what we called the intension set. It defines the content of an utterance. For assertives, [[φ]]g is called the proposition expressed by φ. For questions we can define a corresponding notion. [[?~x.φ]]g is the partition generated by '〈[?~x.φ]〉g . This partition structure is called the issue expressed by ?~x.φ. Definition 10 (Proposition; Issue) Given model M and assignment g, define for each φ ∈ L [[φ]]g = {w | Vw,g(φ) = 1} [[?~x.φ]]g = {{w ∈ W | Vw,g(?~x.φ) = Vv,g(?~x.φ)} | v ∈ W} 2 Incidently, this notion of content can be seen as a projection of the inverse function of the intension applied to the typical result of an utterance of that type, specified by the success 84 CHAPTER 3. INQUIRY conditions. The typical result of an assertion is the truth value 1; a denial has 0. The typical result of a question is a proposition corresponding to a so called nominal answer: a set of tuples of objects that possibly satisfy the question relation. A nominal answer E ⊆ Dn is called possible in case there is some world conceivable that has it as a complete answer. Impossible answers are those which are not supported by any world, for instance because they violate some meaning postulate or because some presuppositions in the question are not satisfied. Definition 11 (Projected Inverse) For any function f :: A → B define f−1 :: B → pow(A), the projected inverse of f , by f−1(b) = {a ∈ A | f (a) = b} (b ∈ B) 2 Proposition 2 (Inverse) For each φ ∈ L, its content is the projection of the inverse intension on a typical result: (i) [[φ]]g = 〈[φ]〉−1 g (1) (ii) [[?~x.φ]]g = { 〈[?~x.φ]〉−1 g {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } Proof (i) 〈[φ]〉−1 g (1) = {w | 〈[φ]〉g(w) = 1} (def) = {w | Vw,g(φ) = 1} (appl, def) = [[φ]]g (*) A nominal answer E ⊆ Dn to some question ?~x.φ is called possible, in case there is some v ∈ W such that Vv,g(λ~x.φ) = E. (ii) { 〈[?~x.φ]〉−1 g ({v | Vv(λ~x.φ) = E}) | E ⊆ Dn, E possible } = {{w | 〈[?~x.φ]〉g(w) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (appl, def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} = Vu,g(?~x.φ)} | u ∈ W} (def, *) = [[?~x.φ]]g 2 What to make of this result? For assertives, it highlights the special role played by the truth value ‘1’. For interrogatives, the function 〈[φ]〉−1 g applied to propositions that describe nominal answers is the identity function. This means that the two ways of generating a partition are essentially the same: grouping worlds that are indistinguishable with respect to an answer, or generating possible answers on the basis of a distribution of nominal answers (provided they are possible) and then finding the worlds to support them. So for the equivalence relation induced by a question, it does not matter whether you take indistinguishability with respect to the n-place abstract or with respect to the question: '〈[λx.φ]〉g = '〈[?x.φ]〉g. The distinction between extensions and intensions of questions is therefore less important for an update semantics with questions, where the equivalence relation is the central notion. 3.3.4 Information States Now we turn to a definition of the set of information states ΣW, on the basis of a given information space W. How are the separate data and structure aspects of an information state to be modelled? The factual information of an agent is modelled by a set of possible worlds: the worlds that are compatible with the information of the agent. This set is called the data set here. The semantic content of a question corresponds to a partition. This has 3.3. UPDATE SEMANTICS WITH QUESTIONS 85 been motivated above. We will use an equivalence relation to model the structure of issues. Partitions and equivalence relations are interchangeable. There is a choice. We can have the equivalence relation range over the complete information space, or over the particular data set of an agent. The choice only makes a difference from the external perspective. The first option makes most sense when issues are public: they are known to each participant on the basis of what was said earlier. Public issues may continue to play a role structuring the information space after they have been resolved for some particular agent. The second option makes most sense when it concerns private issues of an agent. Moreover, questions can contain referential expressions that are only defined in a certain subset of worlds. This context dependency is best modelled by relativising issues to a particular data set. Therefore we take the second option, to limit issues to a particular data set. The aspects of data and structure can now be combined into one equivalence relation σ over a subset of the set of all possible worlds W. From the equival

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Dialogue Models for Inquiry and Transaction

How to decide what to do

In the paper we study the dynamics of roles played by agents in multiagent systems. We capture role dynamics in terms of four operations performed by agents: ‘enactment', ‘deactment', ‘activate', and ‘deactivate'. The use of these operations is motivated, in particular for open systems. A formal semantics for these operations is provided. This formalization is aimed at serving as a basis for implementation of role dynamics in an agent programming language such as 3APL.

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Joris Hulstijn

Papers

The BOID architecture: conflicts between beliefs, obligations, intentions and desires

Goal generation in the BOID architecture

Dialogue Models for Inquiry and Transaction

How to decide what to do

Enacting and deacting roles in agent programming