Software Workshop Team Prolog Pop11

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Software Workshop Team Prolog / Pop11

• Lecture 1 - Introduction

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• Aims of the module
• to enable students to acquire more advanced
  programming skills.
• to give students the experience of working in a
  team to tackle a more challenging project.
• to enable students to develop a deep
  understanding of one major AI technique.
• Objectives
• Develop and document a specification from a set
  of requirements
• Demonstrate understanding of a major AI technique
• Design and implement a project with substantial
  AI content
• Demonstrate advanced programming skills

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Good programming practice AI

• Using AI it is possible to
• Tackle large problems
• Realistic applications
• Big team-based projects
• Example computer games industry
• AI programming needs some careful attention
• Prolog and Pop11 are forgiving languages
• buggy program can still run
• loose syntax

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Software testing AI

• AIl projects typically have one or more of the
  following features
• A poorly defined problem
• A poorly defined goal
• A poorly defined domain
• Often difficult to show that a solution is the
  one you are looking for
• AI project testing needs to focus on two aspects
• Programme testing Does the code do what you
  think it does?
• Solution testing Is what it does the right thing
  to do?

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Teaching methods

• Group presentations
• Lab work
• Weekly supervision meetings
• Unsupervised team projects
• Important note There are no lectures on Mondays
  other than student presentations mid semester and
  end of semester.

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Examples of past project topics

• Playing Draughts
• (Games, Games GA)
• Selly Oak Route Planner
• (Planning)
• Optimal mining
• (Planning Constraint satisfaction)
• Psycho-Analysis Chatterbot
• (Natural Language Processing)

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Project areas

• All projects must feature at least one of the
  following AI techniques
• Planning
• Game playing
• Learning/Decision trees
• Reasoning/Expert systems
• Natural language processing
• Heuristic search
• Semantic networks
• Part of the work in this module is researching
  learning about a such advanced symbolic AI
  technique.

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Assessment

• 5 Exercise Sheet 1
• 5 Exercise Sheet 2
• 20 Group Presentations (1010)
• 70 Group Project Report

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Timetable

• Week 1 Group allocation. Exercise 1 ? 22nd of
  January
• Week 2 Group and project choice. Exercise 2 ?
  29th of January
• Week 3 A specification of the project ? 5th of
  February
• Week 4-5 Initial work
• Week 6 Midway presentation of project ? 19th of
  February
• Week 7-10 Full implementation Testing
• Week 11 Project presentation ?26th of March
• Final report submission ? Thursday 29th of March
  
• All work must be submitted by 5pm.

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Group assessment

• Working in a team is a difficult process
• Assessing work in team is hard too
• Self-Assessment Forms
• These forms have formative role only
  highlighting any problems that need addressing
• ? You should be honest about yourself and your
  team
• ? We dont expect that you work well together
  straight away.

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Final group assessment

• The team will be able to re-allocate 20 of the
  team project mark among the team
• At the end of the project each team member must
  allocate 100 marks to individual team members.
  These will be used to calculate 20 of the final
  mark. Example
• Caveat
• No student can fail of the project passes (40)
• No Shows other extreme cases will be dealt with
  separately (and will achieve a fail
  automatically).

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Late submission

• Late submission will result in a penalty to the
  overall team mark 5 per working day
• Reasonable mitigating circumstances for a single
  student (as per evidence from welfare) will
  waiver the penalty for the entire project