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Task 1: Preparation exploratory analysis of con icts, problems, relations, practices, etc. in: historical perspective; selecting participants; securing participation by stakeholders; establishing relations with the wider policy environment. Task 2: Agreeing upon a process design and process protocol creating an agreed-upon code of conduct and provisional agenda; reaching agreement about procedures, methodologies, etc.; process management and maintenance of process agreements; securing new process agreements as the process unfolds. Task 3: Joint exploration and situation analysis group formation; exchanging perspectives, interests, goals; analysing problems and interrelations; integration of visions into new problem de nitions; preliminary identi cation of alternative solutions and win win strategies; identi cation of gaps in knowledge and insight. Task 4: Joint fact- nding developing and implementing action plans to ll knowledge gaps. Task 5: Forging agreement manoeuvre: clarifying positions, making claims, use of pressure to secure concessions, create and resolve impasses; securing agreement on a coherent package of measures and action plans. Task 6: Communication of representatives with constituencies transferring the learning process; rati cation of agreement by constituencies. Task 7: Monitoring implementation implementing the agreements made; monitoring progress; creating contexts of.

But policy makers must generally form their policies in ignorance of what the actual effect of their policies will be, and, in particular, the actual effect may be something quite different to that intended In the greenhouse gas example, the effect of increasing fuel taxes might be to cause consumers to switch to cheaper - dirtier - fuel types, at best causing no overall reduction in the release of greenhouse gases, and potentially even leading to an increase So, it is proposed, multiagent simulation models might fruitfully be used to gain an understanding of the effect of their nascent policies An example of such a system is the Freshwater Integrated Resource Management with Agents (FIRMA) project (Downing et al, 2001) This project is specifically intended to understand the impact of governments exhorting water consumers to exercise care and caution in water use during times of drought (Downing et al.

by considering in (3.13).

mon approaches, but certain themes did recur in this work Recurring themes were the rejection of architectures based on symbolic representations, an emphasis on a closer coupling between the agent's environment and the action it performs, and the idea that intelligent behaviour can be seen to emerge from the interaction of a number of much simpler behaviours The term 'furore' might reasonably be used to describe the response from the symbolic and logical reasoning communities to the emergence of behavioural AI Some researchers seemed to feel that behavioural AI was a direct challenge to the beliefs and assumptions that had shaped their entire academic careers Not surprisingly, they were not predisposed simply to abandon their ideas and research programs I do not believe there was (or is) a clear cut 'right' or 'wrong' in this debate.

, 2001, p 206) (In case you were wondering, yes, droughts do happen in the UK!) Downing et al (2001) developed a multiagent simulation model in which water consumers were represented by agents, and a 'policy' agent issued exhortations to consume less at times of drought The authors were able to develop a simulation model that fairly closely resembled the observed behaviour in human societies in similar circumstances; developing this model was an iterative process of model reformulation followed by a review of the observed results of the model with water utilities..

Q1-element on a convex quadrilateralK Consider an arbitrary convex quadrilats2,. eral domain K c R2 with straight edges sl, . . , s4, illustrated in Figure 3.3.

With the benefit of hindsight, it seems clear that much symbolic A1 research had wandered into the realms of abstract theory, and did not connect in any realistic way with the reality of building and deploymg agents in realistic scenarios It also seems clear that the decomposition of A1 into components such as planning and learning, without any emphasis on synthesizing these components into an integrated architecture, was perhaps not the best strategy for A1 as a discipline But it also seems that some claims made by members of the behavioural community were extreme, and in many cases suffered from the over-optimism that A1 itself suffered from in its early days The practical implications of all this were threefold The first was that the behavioural A community to a certain extent split 1 off from the mainstream AI community.

Web Layout is designed for composing and reviewing documents that will be viewed online rather than printed. Hence, information such as page and section numbers is excluded from the status bar. If the document contains hyperlinks, they are displayed underlined by default, as shown in Figure 4-25. Background colors, pictures, and textures are also displayed.