tailieunhanh - Socially Intel. Agents Creating Rels. with Comp. & Robots - Dautenhahn et al (Eds) Part 7

Tham khảo tài liệu 'socially intel. agents creating rels. with comp. & robots - dautenhahn et al (eds) part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 104 Socially Intelligent Agents 4 Tambe s proxy automatically volunteered him for a presentation though he was actually unwilling. Again had over-generalized from a few examples and when a timeout occurred had taken an undesirable autonomous action. From the growing list of failures it became clear that the approach faced some fundamental problems. The first problem was the AA coordination challenge. Learning from user input when combined with timeouts failed to address the challenge since the agent sometimes had to take autonomous actions although it was ill-prepared to do so examples 2 and 4 . Second the approach did not consider the team cost of erroneous autonomous actions examples 1 and 2 . Effective agent AA needs explicit reasoning and careful tradeoffs when dealing with the different individual and team costs and uncertainties. Third decisiontree learning lacked the lookahead ability to plan actions that may work better over the longer term. For instance in example 3 each five-minute delay is appropriate in isolation but the rules did not consider the ramifications of one action on successive actions. Planning could have resulted in a one-hour delay instead of many five-minute delays. Planning and consideration of cost could also lead to an agent taking the low-cost action of a short meeting delay while it consults the user regarding the higher-cost cancel action example 1 . 4. MDPs for Adjustable Autonomy Figure . Dialog for meetings highest reward high reward Figure . A small portion of simplified version of the delay MDP MDPs were a natural choice for addressing the issues identified in the previous section reasoning about the costs of actions handling uncertainty planning for future outcomes and encoding domain knowledge. The delay MDP typical of MDPs in Friday represents a class of MDPs covering all types of meetings for which the agent may take rescheduling actions. For each meeting an agent can autonomously perform any of the 10 actions